{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Updated Voting Data Analysis\n",
    "\n",
    "## By Brandon Zhao\n",
    "\n",
    "### Analyses will be organized by:\n",
    "\n",
    "1) Title\n",
    "\n",
    "2) chunk of code\n",
    "\n",
    "3) bivariate regression table\n",
    "\n",
    "4) bivariate scatterplot\n",
    "\n",
    "5) multivariate regression table (with the exception of analyses grouped cities)\n",
    "\n",
    "### The analyses included (in order) are:\n",
    "\n",
    "Male Mortality and Cities over 100000\n",
    "\n",
    "Male Mortality and Cities over 100000 plus Smaller Cities\n",
    "\n",
    "Male Mortality and Cities over 100000 plus Smaller Cities plus Grouped Cities\n",
    "\n",
    "Male Mortality and Smaller Cities plus Grouped Cities\n",
    "\n",
    "Male Mortality and Cities over 100000 plus Grouped Cities\n",
    "\n",
    "Total Mortality and Cities over 100000\n",
    "\n",
    "Total Mortality and Cities over 100000 plus Smaller Cities\n",
    "\n",
    "Total Mortality and Cities over 100000 plus Smaller Cities plus Grouped Cities\n",
    "\n",
    "Total Mortality and Smaller Cities plus Grouped Cities\n",
    "\n",
    "Total Mortality and Cities over 100000 plus Grouped Cities\n",
    "\n",
    "Male TB Mortality and Cities over 100000\n",
    "\n",
    "Male TB Mortality and Cities over 100000 plus Smaller Cities\n",
    "\n",
    "Male TB Mortality and Cities over 100000 plus Smaller Cities plus Grouped Cities\n",
    "\n",
    "Male TB Mortality and Smaller Cities plus Grouped Cities\n",
    "\n",
    "Male TB Mortality and Cities over 100000 plus Grouped Cities\n",
    "\n",
    "Total TB Mortality and Cities over 100000\n",
    "\n",
    "Total TB Mortality and Cities over 100000 plus Smaller Cities\n",
    "\n",
    "Total TB Mortality and Cities over 100000 plus Smaller Cities plus Grouped Cities\n",
    "\n",
    "Total TB Mortality and Smaller Cities plus Grouped Cities\n",
    "\n",
    "Total TB Mortality and Cities over 100000 plus Grouped Cities\n",
    "\n",
    "Total TB Mortality Outlier Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.graphics.regressionplots import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "\n",
    "# df_100000 = pd.read_csv(\"CITIES_OVER_100000.csv\")\n",
    "# df_ungrouped = pd.read_csv(\"CITIES_OVER_100000_AND_SMALLER_CITIES.csv\")\n",
    "# df_all = pd.read_csv(\"CITIES_OVER_100000_AND_SMALLER_CITIES_AND_GROUPED.csv\")\n",
    "# df_100000_grouped = pd.read_csv(\"CITIES_OVER_100000_AND_GROUPED_CITIES.csv\")\n",
    "# df_small_grouped = pd.read_csv(\"SMALLER_CITIES_AND_GROUPED_CITIES.csv\")\n",
    "\n",
    "df_all = pd.read_csv(\"~/Downloads/final-lebensraum-data.csv\")\n",
    "# df_total = pd.read_csv(\"~/Downloads/CITIES_OVER_100000_AND_SMALLER_CITIES_AND_GROUPED.csv\")\n",
    "df_100000 = df_all.query('over_100000 == True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RESULTS FOR MALE MORTALITY AS INDEPENDENT VARIABLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male Mortality and Cities over 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.006\n",
      "Model:                            OLS   Adj. R-squared:                 -0.027\n",
      "Method:                 Least Squares   F-statistic:                    0.1728\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.681\n",
      "Time:                        22:29:39   Log-Likelihood:                -106.70\n",
      "No. Observations:                  32   AIC:                             217.4\n",
      "Df Residuals:                      30   BIC:                             220.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept           7.2076      9.557      0.754      0.457     -12.310      26.725\n",
      "ratio_mort_male     3.8809      9.337      0.416      0.681     -15.188      22.950\n",
      "==============================================================================\n",
      "Omnibus:                       18.775   Durbin-Watson:                   2.406\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               24.544\n",
      "Skew:                           1.557   Prob(JB):                     4.68e-06\n",
      "Kurtosis:                       5.951   Cond. No.                         15.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.113\n",
      "Model:                            OLS   Adj. R-squared:                  0.018\n",
      "Method:                 Least Squares   F-statistic:                     1.187\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.332\n",
      "Time:                        22:29:39   Log-Likelihood:                -104.87\n",
      "No. Observations:                  32   AIC:                             217.7\n",
      "Df Residuals:                      28   BIC:                             223.6\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept         -10.8470     13.561     -0.800      0.431     -38.626      16.932\n",
      "ratio_mort_male     5.2725      9.464      0.557      0.582     -14.114      24.659\n",
      "prop_catholic      -1.5486      4.549     -0.340      0.736     -10.867       7.770\n",
      "prop_workers       35.2889     19.465      1.813      0.081      -4.583      75.161\n",
      "==============================================================================\n",
      "Omnibus:                       15.204   Durbin-Watson:                   1.990\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               16.470\n",
      "Skew:                           1.412   Prob(JB):                     0.000265\n",
      "Kurtosis:                       5.091   Cond. No.                         28.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort_male\", data = df_100000).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000.ratio_mort_male, df_100000.change_votes)\n",
    "plt.xlabel(\"Ratio Mort Male \")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort Male vs Change Votes for Cities over 100000\")\n",
    "plt.plot([.6, 1.5], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 1.5])\n",
    "plt.show()\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort_male + prop_catholic + prop_workers\", data = df_100000).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male Mortality and Cities over 100000 plus Smaller Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                 -0.026\n",
      "Method:                 Least Squares   F-statistic:                  0.006370\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.937\n",
      "Time:                        22:29:39   Log-Likelihood:                -133.41\n",
      "No. Observations:                  40   AIC:                             270.8\n",
      "Df Residuals:                      38   BIC:                             274.2\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept          10.9199      6.813      1.603      0.117      -2.871      24.711\n",
      "ratio_mort_male     0.5235      6.559      0.080      0.937     -12.755      13.802\n",
      "==============================================================================\n",
      "Omnibus:                       16.800   Durbin-Watson:                   2.328\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               20.453\n",
      "Skew:                           1.353   Prob(JB):                     3.62e-05\n",
      "Kurtosis:                       5.224   Cond. No.                         12.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.050\n",
      "Model:                            OLS   Adj. R-squared:                 -0.030\n",
      "Method:                 Least Squares   F-statistic:                    0.6268\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.602\n",
      "Time:                        22:29:39   Log-Likelihood:                -132.39\n",
      "No. Observations:                  40   AIC:                             272.8\n",
      "Df Residuals:                      36   BIC:                             279.5\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept          -1.1350     12.016     -0.094      0.925     -25.504      23.234\n",
      "ratio_mort_male     3.4079      6.904      0.494      0.625     -10.594      17.410\n",
      "prop_catholic      -2.9694      4.265     -0.696      0.491     -11.620       5.681\n",
      "prop_workers       20.6698     16.654      1.241      0.223     -13.107      54.447\n",
      "==============================================================================\n",
      "Omnibus:                       15.902   Durbin-Watson:                   2.058\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               18.059\n",
      "Skew:                           1.366   Prob(JB):                     0.000120\n",
      "Kurtosis:                       4.837   Cond. No.                         27.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort_male\", data = df_ungrouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_ungrouped.ratio_mort_male, df_ungrouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort Male \")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort Male vs Change Votes for Cities over 100000 Plus Smaller Cities\")\n",
    "plt.plot([.6, 1.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 1.6])\n",
    "plt.show()\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort_male + prop_catholic + prop_workers\", data = df_ungrouped).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male Mortality and Cities over 100000 plus Smaller Cities plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.005\n",
      "Model:                            OLS   Adj. R-squared:                 -0.014\n",
      "Method:                 Least Squares   F-statistic:                    0.2615\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.611\n",
      "Time:                        22:29:39   Log-Likelihood:                -179.13\n",
      "No. Observations:                  53   AIC:                             362.3\n",
      "Df Residuals:                      51   BIC:                             366.2\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept           8.3011      6.306      1.316      0.194      -4.358      20.960\n",
      "ratio_mort_male     3.1048      6.071      0.511      0.611      -9.084      15.293\n",
      "==============================================================================\n",
      "Omnibus:                       18.465   Durbin-Watson:                   2.529\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               23.272\n",
      "Skew:                           1.332   Prob(JB):                     8.84e-06\n",
      "Kurtosis:                       4.855   Cond. No.                         12.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort_male\", data = df_all).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_all.ratio_mort_male, df_all.change_votes)\n",
    "plt.xlabel(\"Ratio Mort Male \")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort Male vs Change Votes for Cities over 100000 Plus Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 1.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 1.6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male Mortality and Smaller Cities plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.004\n",
      "Model:                            OLS   Adj. R-squared:                 -0.049\n",
      "Method:                 Least Squares   F-statistic:                   0.07034\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.794\n",
      "Time:                        22:29:40   Log-Likelihood:                -72.209\n",
      "No. Observations:                  21   AIC:                             148.4\n",
      "Df Residuals:                      19   BIC:                             150.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept           9.6279      9.117      1.056      0.304      -9.455      28.711\n",
      "ratio_mort_male     2.2787      8.592      0.265      0.794     -15.704      20.262\n",
      "==============================================================================\n",
      "Omnibus:                        6.217   Durbin-Watson:                   2.704\n",
      "Prob(Omnibus):                  0.045   Jarque-Bera (JB):                4.076\n",
      "Skew:                           1.029   Prob(JB):                        0.130\n",
      "Kurtosis:                       3.651   Cond. No.                         10.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort_male\", data = df_small_grouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_small_grouped.ratio_mort_male, df_small_grouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort Male \")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort Male vs Change Votes for Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 1.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 1.6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male Mortality and Cities over 100000 plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000 PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.018\n",
      "Model:                            OLS   Adj. R-squared:                 -0.004\n",
      "Method:                 Least Squares   F-statistic:                    0.8060\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.374\n",
      "Time:                        22:29:40   Log-Likelihood:                -152.21\n",
      "No. Observations:                  45   AIC:                             308.4\n",
      "Df Residuals:                      43   BIC:                             312.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept           4.0785      8.084      0.505      0.616     -12.224      20.381\n",
      "ratio_mort_male     7.0601      7.864      0.898      0.374      -8.799      22.919\n",
      "==============================================================================\n",
      "Omnibus:                       18.732   Durbin-Watson:                   2.368\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               23.853\n",
      "Skew:                           1.436   Prob(JB):                     6.61e-06\n",
      "Kurtosis:                       5.116   Cond. No.                         14.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort_male\", data = df_100000_grouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR MALE MORTALITY AND CITIES OVER 100000 PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000_grouped.ratio_mort_male, df_100000_grouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort Male \")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort Male vs Change Votes for Cities Over 100000 Plus Grouped\")\n",
    "plt.plot([.6, 1.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 1.6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RESULTS FOR TOTAL MORTALITY AS INDEPENDENT VARIABLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Mortality and Cities over 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.007\n",
      "Model:                            OLS   Adj. R-squared:                 -0.026\n",
      "Method:                 Least Squares   F-statistic:                    0.2011\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.657\n",
      "Time:                        22:29:40   Log-Likelihood:                -106.68\n",
      "No. Observations:                  32   AIC:                             217.4\n",
      "Df Residuals:                      30   BIC:                             220.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      6.4368     10.575      0.609      0.547     -15.159      28.033\n",
      "ratio_mort     4.4571      9.939      0.448      0.657     -15.841      24.755\n",
      "==============================================================================\n",
      "Omnibus:                       17.975   Durbin-Watson:                   2.411\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               22.777\n",
      "Skew:                           1.509   Prob(JB):                     1.13e-05\n",
      "Kurtosis:                       5.825   Cond. No.                         17.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.128\n",
      "Model:                            OLS   Adj. R-squared:                  0.035\n",
      "Method:                 Least Squares   F-statistic:                     1.370\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.272\n",
      "Time:                        22:29:40   Log-Likelihood:                -104.60\n",
      "No. Observations:                  32   AIC:                             217.2\n",
      "Df Residuals:                      28   BIC:                             223.1\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept       -16.4939     15.520     -1.063      0.297     -48.286      15.298\n",
      "ratio_mort        8.9976     10.048      0.895      0.378     -11.584      29.579\n",
      "prop_catholic    -1.7082      4.449     -0.384      0.704     -10.821       7.405\n",
      "prop_workers     38.4624     19.693      1.953      0.061      -1.877      78.802\n",
      "==============================================================================\n",
      "Omnibus:                       14.774   Durbin-Watson:                   1.962\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               15.736\n",
      "Skew:                           1.386   Prob(JB):                     0.000383\n",
      "Kurtosis:                       5.030   Cond. No.                         31.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort\", data = df_100000).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000.ratio_mort, df_100000.change_votes)\n",
    "plt.xlabel(\"Ratio Mort\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort vs Change Votes for Cities over 100000\")\n",
    "plt.plot([.6, 1.5], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 1.5])\n",
    "plt.show()\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort + prop_catholic + prop_workers\", data = df_100000).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Mortality and Cities over 100000 plus Smaller Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.004\n",
      "Model:                            OLS   Adj. R-squared:                 -0.022\n",
      "Method:                 Least Squares   F-statistic:                    0.1604\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.691\n",
      "Time:                        22:29:40   Log-Likelihood:                -133.33\n",
      "No. Observations:                  40   AIC:                             270.7\n",
      "Df Residuals:                      38   BIC:                             274.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      8.0861      8.487      0.953      0.347      -9.095      25.267\n",
      "ratio_mort     3.1668      7.907      0.400      0.691     -12.840      19.174\n",
      "==============================================================================\n",
      "Omnibus:                       17.202   Durbin-Watson:                   2.327\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.093\n",
      "Skew:                           1.386   Prob(JB):                     2.63e-05\n",
      "Kurtosis:                       5.229   Cond. No.                         15.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.066\n",
      "Model:                            OLS   Adj. R-squared:                 -0.011\n",
      "Method:                 Least Squares   F-statistic:                    0.8538\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.474\n",
      "Time:                        22:29:40   Log-Likelihood:                -132.04\n",
      "No. Observations:                  40   AIC:                             272.1\n",
      "Df Residuals:                      36   BIC:                             278.8\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept        -7.7612     14.062     -0.552      0.584     -36.279      20.757\n",
      "ratio_mort        8.0272      8.484      0.946      0.350      -9.180      25.234\n",
      "prop_catholic    -3.2195      4.229     -0.761      0.451     -11.797       5.358\n",
      "prop_workers     24.1108     16.953      1.422      0.164     -10.272      58.494\n",
      "==============================================================================\n",
      "Omnibus:                       15.988   Durbin-Watson:                   2.039\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               18.161\n",
      "Skew:                           1.377   Prob(JB):                     0.000114\n",
      "Kurtosis:                       4.820   Cond. No.                         30.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort\", data = df_ungrouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_ungrouped.ratio_mort, df_ungrouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort vs Change Votes for Cities over 100000 Plus Smaller Cities\")\n",
    "plt.plot([.6, 1.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 1.6])\n",
    "plt.show()\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort + prop_catholic + prop_workers\", data = df_ungrouped).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Mortality and Cities over 100000 plus Smaller Cities plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.022\n",
      "Model:                            OLS   Adj. R-squared:                  0.003\n",
      "Method:                 Least Squares   F-statistic:                     1.142\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.290\n",
      "Time:                        22:29:40   Log-Likelihood:                -178.68\n",
      "No. Observations:                  53   AIC:                             361.4\n",
      "Df Residuals:                      51   BIC:                             365.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      3.1746      7.840      0.405      0.687     -12.566      18.915\n",
      "ratio_mort     7.7880      7.289      1.068      0.290      -6.845      22.421\n",
      "==============================================================================\n",
      "Omnibus:                       18.191   Durbin-Watson:                   2.534\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               22.578\n",
      "Skew:                           1.331   Prob(JB):                     1.25e-05\n",
      "Kurtosis:                       4.771   Cond. No.                         15.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort\", data = df_all).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_all.ratio_mort, df_all.change_votes)\n",
    "plt.xlabel(\"Ratio Mort\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort vs Change Votes for Cities over 100000 Plus Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 1.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 1.6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Mortality and Smaller Cities plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.046\n",
      "Model:                            OLS   Adj. R-squared:                 -0.004\n",
      "Method:                 Least Squares   F-statistic:                    0.9158\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.351\n",
      "Time:                        22:29:41   Log-Likelihood:                -71.754\n",
      "No. Observations:                  21   AIC:                             147.5\n",
      "Df Residuals:                      19   BIC:                             149.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.2155     12.432      0.017      0.986     -25.806      26.237\n",
      "ratio_mort    10.8819     11.371      0.957      0.351     -12.919      34.682\n",
      "==============================================================================\n",
      "Omnibus:                        7.410   Durbin-Watson:                   2.674\n",
      "Prob(Omnibus):                  0.025   Jarque-Bera (JB):                5.101\n",
      "Skew:                           1.139   Prob(JB):                       0.0780\n",
      "Kurtosis:                       3.801   Cond. No.                         14.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort\", data = df_small_grouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_small_grouped.ratio_mort, df_small_grouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort vs Change Votes for Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 1.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 1.6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Mortality and Cities over 100000 plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000 PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.034\n",
      "Model:                            OLS   Adj. R-squared:                  0.011\n",
      "Method:                 Least Squares   F-statistic:                     1.500\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.227\n",
      "Time:                        22:29:41   Log-Likelihood:                -151.86\n",
      "No. Observations:                  45   AIC:                             307.7\n",
      "Df Residuals:                      43   BIC:                             311.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0077      9.271     -0.001      0.999     -18.704      18.688\n",
      "ratio_mort    10.6174      8.669      1.225      0.227      -6.865      28.100\n",
      "==============================================================================\n",
      "Omnibus:                       17.147   Durbin-Watson:                   2.374\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               20.628\n",
      "Skew:                           1.359   Prob(JB):                     3.32e-05\n",
      "Kurtosis:                       4.901   Cond. No.                         17.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ ratio_mort\", data = df_100000_grouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL MORTALITY AND CITIES OVER 100000 PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000_grouped.ratio_mort, df_100000_grouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort vs Change Votes for Cities Over 100000 Plus Grouped\")\n",
    "plt.plot([.6, 1.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 1.6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RESULTS FOR MALE TB MORTALITY AS INDEPENDENT VARIABLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male TB Mortality and Cities over 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.102\n",
      "Model:                            OLS   Adj. R-squared:                  0.066\n",
      "Method:                 Least Squares   F-statistic:                     2.848\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.104\n",
      "Time:                        22:29:41   Log-Likelihood:                -89.107\n",
      "No. Observations:                  27   AIC:                             182.2\n",
      "Df Residuals:                      25   BIC:                             184.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.0317      6.476      0.159      0.875     -12.305      14.369\n",
      "male_tb        7.7699      4.604      1.688      0.104      -1.713      17.252\n",
      "==============================================================================\n",
      "Omnibus:                       12.701   Durbin-Watson:                   2.399\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               11.694\n",
      "Skew:                           1.397   Prob(JB):                      0.00289\n",
      "Kurtosis:                       4.610   Cond. No.                         10.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.177\n",
      "Model:                            OLS   Adj. R-squared:                  0.069\n",
      "Method:                 Least Squares   F-statistic:                     1.647\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.206\n",
      "Time:                        22:29:41   Log-Likelihood:                -87.937\n",
      "No. Observations:                  27   AIC:                             183.9\n",
      "Df Residuals:                      23   BIC:                             189.1\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept       -11.7071     11.746     -0.997      0.329     -36.006      12.592\n",
      "male_tb           5.0094      5.001      1.002      0.327      -5.336      15.355\n",
      "prop_catholic    -1.5115      4.490     -0.337      0.739     -10.799       7.776\n",
      "prop_workers     35.2657     25.556      1.380      0.181     -17.602      88.133\n",
      "==============================================================================\n",
      "Omnibus:                       12.587   Durbin-Watson:                   2.212\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               11.559\n",
      "Skew:                           1.375   Prob(JB):                      0.00309\n",
      "Kurtosis:                       4.645   Cond. No.                         38.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ male_tb\", data = df_100000).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000.male_tb, df_100000.change_votes)\n",
    "plt.xlabel(\"Ratio Mort Male TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort Male TB vs Change Votes for Cities over 100000\")\n",
    "plt.plot([.6, 2.2], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.2])\n",
    "plt.show()\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ male_tb + prop_catholic + prop_workers\", data = df_100000).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male TB Mortality and Cities over 100000 plus Smaller Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.037\n",
      "Model:                            OLS   Adj. R-squared:                  0.006\n",
      "Method:                 Least Squares   F-statistic:                     1.194\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.283\n",
      "Time:                        22:29:41   Log-Likelihood:                -110.32\n",
      "No. Observations:                  33   AIC:                             224.6\n",
      "Df Residuals:                      31   BIC:                             227.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      7.5350      4.452      1.692      0.101      -1.546      16.616\n",
      "male_tb        3.3794      3.092      1.093      0.283      -2.927       9.686\n",
      "==============================================================================\n",
      "Omnibus:                       14.694   Durbin-Watson:                   2.319\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               15.409\n",
      "Skew:                           1.412   Prob(JB):                     0.000451\n",
      "Kurtosis:                       4.798   Cond. No.                         7.59\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.107\n",
      "Model:                            OLS   Adj. R-squared:                  0.015\n",
      "Method:                 Least Squares   F-statistic:                     1.159\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.342\n",
      "Time:                        22:29:41   Log-Likelihood:                -109.07\n",
      "No. Observations:                  33   AIC:                             226.1\n",
      "Df Residuals:                      29   BIC:                             232.1\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept        -2.2921     10.140     -0.226      0.823     -23.031      18.447\n",
      "male_tb           2.3883      3.150      0.758      0.454      -4.053       8.830\n",
      "prop_catholic    -3.9100      4.432     -0.882      0.385     -12.974       5.154\n",
      "prop_workers     25.8795     20.770      1.246      0.223     -16.600      68.359\n",
      "==============================================================================\n",
      "Omnibus:                       12.426   Durbin-Watson:                   2.129\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               11.908\n",
      "Skew:                           1.303   Prob(JB):                      0.00260\n",
      "Kurtosis:                       4.367   Cond. No.                         33.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ male_tb\", data = df_ungrouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_ungrouped.male_tb, df_ungrouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort Male TB \")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort Male TB vs Change Votes for Cities over 100000 Plus Smaller Cities\")\n",
    "plt.plot([.6, 3], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 3])\n",
    "plt.show()\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ male_tb + prop_catholic + prop_workers\", data = df_ungrouped).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male TB Mortality and Cities over 100000 plus Smaller Cities plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.011\n",
      "Model:                            OLS   Adj. R-squared:                 -0.013\n",
      "Method:                 Least Squares   F-statistic:                    0.4677\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.498\n",
      "Time:                        22:29:41   Log-Likelihood:                -149.94\n",
      "No. Observations:                  44   AIC:                             303.9\n",
      "Df Residuals:                      42   BIC:                             307.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      9.5630      4.014      2.382      0.022       1.462      17.664\n",
      "male_tb        1.8821      2.752      0.684      0.498      -3.672       7.436\n",
      "==============================================================================\n",
      "Omnibus:                       13.885   Durbin-Watson:                   2.536\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               14.728\n",
      "Skew:                           1.229   Prob(JB):                     0.000634\n",
      "Kurtosis:                       4.411   Cond. No.                         7.50\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ male_tb\", data = df_all).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_all.male_tb, df_all.change_votes)\n",
    "plt.xlabel(\"Ratio Mort Male TB \")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort Male TB vs Change Votes for Cities over 100000 Plus Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 3], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male TB Mortality and Smaller Cities plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.003\n",
      "Model:                            OLS   Adj. R-squared:                 -0.064\n",
      "Method:                 Least Squares   F-statistic:                   0.04129\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.842\n",
      "Time:                        22:29:41   Log-Likelihood:                -59.264\n",
      "No. Observations:                  17   AIC:                             122.5\n",
      "Df Residuals:                      15   BIC:                             124.2\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     14.0159      5.698      2.460      0.027       1.872      26.160\n",
      "male_tb       -0.7531      3.706     -0.203      0.842      -8.652       7.146\n",
      "==============================================================================\n",
      "Omnibus:                        2.421   Durbin-Watson:                   2.587\n",
      "Prob(Omnibus):                  0.298   Jarque-Bera (JB):                1.321\n",
      "Skew:                           0.683   Prob(JB):                        0.516\n",
      "Kurtosis:                       3.031   Cond. No.                         5.94\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Brandon\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ male_tb\", data = df_small_grouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_small_grouped.male_tb, df_small_grouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort Male TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort Male TB vs Change Votes for Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 3], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male TB Mortality and Cities over 100000 plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000 PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.020\n",
      "Model:                            OLS   Adj. R-squared:                 -0.007\n",
      "Method:                 Least Squares   F-statistic:                    0.7505\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.392\n",
      "Time:                        22:29:42   Log-Likelihood:                -129.43\n",
      "No. Observations:                  38   AIC:                             262.9\n",
      "Df Residuals:                      36   BIC:                             266.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      7.5098      5.161      1.455      0.154      -2.958      17.977\n",
      "male_tb        3.1093      3.589      0.866      0.392      -4.170      10.388\n",
      "==============================================================================\n",
      "Omnibus:                       14.247   Durbin-Watson:                   2.375\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               15.066\n",
      "Skew:                           1.312   Prob(JB):                     0.000535\n",
      "Kurtosis:                       4.620   Cond. No.                         8.95\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ male_tb\", data = df_100000_grouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR MALE TB MORTALITY AND CITIES OVER 100000 PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000_grouped.male_tb, df_100000_grouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort Male TB \")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort Male TB vs Change Votes for Cities Over 100000 Plus Grouped\")\n",
    "plt.plot([.6, 2.7], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.7])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RESULTS FOR TOTAL TB MORTALITY AS INDEPENDENT VARIABLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total TB Mortality and Cities over 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.149\n",
      "Model:                            OLS   Adj. R-squared:                  0.116\n",
      "Method:                 Least Squares   F-statistic:                     4.553\n",
      "Date:                Tue, 16 Aug 2022   Prob (F-statistic):             0.0425\n",
      "Time:                        22:54:54   Log-Likelihood:                -91.676\n",
      "No. Observations:                  28   AIC:                             187.4\n",
      "Df Residuals:                      26   BIC:                             190.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -3.3359      7.055     -0.473      0.640     -17.837      11.165\n",
      "total_tb       9.2210      4.322      2.134      0.042       0.338      18.104\n",
      "==============================================================================\n",
      "Omnibus:                       13.884   Durbin-Watson:                   2.418\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               13.540\n",
      "Skew:                           1.453   Prob(JB):                      0.00115\n",
      "Kurtosis:                       4.779   Cond. No.                         12.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.227\n",
      "Model:                            OLS   Adj. R-squared:                  0.131\n",
      "Method:                 Least Squares   F-statistic:                     2.355\n",
      "Date:                Tue, 16 Aug 2022   Prob (F-statistic):             0.0971\n",
      "Time:                        22:54:54   Log-Likelihood:                -90.322\n",
      "No. Observations:                  28   AIC:                             188.6\n",
      "Df Residuals:                      24   BIC:                             194.0\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept       -14.7175     10.792     -1.364      0.185     -36.992       7.557\n",
      "total_tb          6.5144      4.777      1.364      0.185      -3.344      16.373\n",
      "prop_catholic    -2.1003      4.356     -0.482      0.634     -11.091       6.891\n",
      "prop_workers     34.1861     23.825      1.435      0.164     -14.987      83.360\n",
      "==============================================================================\n",
      "Omnibus:                       14.536   Durbin-Watson:                   2.233\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               14.671\n",
      "Skew:                           1.465   Prob(JB):                     0.000652\n",
      "Kurtosis:                       4.999   Cond. No.                         40.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_100000).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000.total_tb, df_100000.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities over 100000\")\n",
    "plt.plot([.9, 2.4], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.4])\n",
    "plt.show()\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ total_tb + prop_catholic + prop_workers\", data = df_100000).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do that with some LOGS too!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.009\n",
      "Model:                            OLS   Adj. R-squared:                 -0.031\n",
      "Method:                 Least Squares   F-statistic:                    0.2203\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.643\n",
      "Time:                        22:29:42   Log-Likelihood:                -90.445\n",
      "No. Observations:                  27   AIC:                             184.9\n",
      "Df Residuals:                      25   BIC:                             187.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept             6.9192     10.350      0.669      0.510     -14.397      28.235\n",
      "log_diff_total_tb     0.8878      1.892      0.469      0.643      -3.008       4.784\n",
      "==============================================================================\n",
      "Omnibus:                       17.320   Durbin-Watson:                   2.497\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.048\n",
      "Skew:                           1.526   Prob(JB):                     2.69e-05\n",
      "Kurtosis:                       6.065   Cond. No.                         42.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ log_diff_total_tb\", data = df_100000).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000.log_diff_total_tb, df_100000.change_votes)\n",
    "plt.xlabel(\"LN of (Total TB Mortality 1918 - Total TB Mortality 1914)\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"LN First Difference Total TB Mortality vs Change Votes for Cities over 100000\")\n",
    "plt.plot([3.75, 7], [reg.params[0] + reg.params[1] * 3.75, reg.params[0] + reg.params[1] * 7])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             diff_votes   R-squared:                       0.004\n",
      "Model:                            OLS   Adj. R-squared:                 -0.035\n",
      "Method:                 Least Squares   F-statistic:                    0.1090\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.744\n",
      "Time:                        22:29:42   Log-Likelihood:                 55.319\n",
      "No. Observations:                  27   AIC:                            -106.6\n",
      "Df Residuals:                      25   BIC:                            -104.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept             0.1156      0.047      2.469      0.021       0.019       0.212\n",
      "log_diff_total_tb    -0.0028      0.009     -0.330      0.744      -0.020       0.015\n",
      "==============================================================================\n",
      "Omnibus:                        2.206   Durbin-Watson:                   1.799\n",
      "Prob(Omnibus):                  0.332   Jarque-Bera (JB):                1.364\n",
      "Skew:                          -0.267   Prob(JB):                        0.506\n",
      "Kurtosis:                       2.038   Cond. No.                         42.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"diff_votes ~ log_diff_total_tb\", data = df_100000).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000.log_diff_total_tb, df_100000.diff_votes)\n",
    "plt.xlabel(\"LN of (Total TB Mortality 1918 - Total TB Mortality 1914)\")\n",
    "plt.ylabel(\"Difference in Votes\")\n",
    "plt.title(\"LN First Difference Total TB Mortality vs Difference in Votes for Cities over 100000\")\n",
    "plt.plot([3.75, 7], [reg.params[0] + reg.params[1] * 3.75, reg.params[0] + reg.params[1] * 7])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total TB Mortality and Cities over 100000 plus Smaller Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.041\n",
      "Model:                            OLS   Adj. R-squared:                  0.010\n",
      "Method:                 Least Squares   F-statistic:                     1.327\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.258\n",
      "Time:                        22:29:42   Log-Likelihood:                -110.25\n",
      "No. Observations:                  33   AIC:                             224.5\n",
      "Df Residuals:                      31   BIC:                             227.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      5.5524      5.911      0.939      0.355      -6.503      17.608\n",
      "total_tb       4.1436      3.598      1.152      0.258      -3.194      11.481\n",
      "==============================================================================\n",
      "Omnibus:                       14.552   Durbin-Watson:                   2.311\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               15.151\n",
      "Skew:                           1.410   Prob(JB):                     0.000513\n",
      "Kurtosis:                       4.751   Cond. No.                         10.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.107\n",
      "Model:                            OLS   Adj. R-squared:                  0.015\n",
      "Method:                 Least Squares   F-statistic:                     1.163\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.341\n",
      "Time:                        22:29:43   Log-Likelihood:                -109.07\n",
      "No. Observations:                  33   AIC:                             226.1\n",
      "Df Residuals:                      29   BIC:                             232.1\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept        -2.4145     10.174     -0.237      0.814     -23.223      18.394\n",
      "total_tb          2.9058      3.797      0.765      0.450      -4.860      10.671\n",
      "prop_catholic    -4.3362      4.412     -0.983      0.334     -13.359       4.687\n",
      "prop_workers     23.5002     21.562      1.090      0.285     -20.599      67.599\n",
      "==============================================================================\n",
      "Omnibus:                       12.648   Durbin-Watson:                   2.132\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               12.221\n",
      "Skew:                           1.315   Prob(JB):                      0.00222\n",
      "Kurtosis:                       4.404   Cond. No.                         37.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_ungrouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_ungrouped.total_tb, df_ungrouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities over 100000 Plus Smaller Cities\")\n",
    "plt.plot([.6, 2.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.6])\n",
    "plt.show()\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ total_tb + prop_catholic + prop_workers\", data = df_ungrouped).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total TB Mortality and Cities over 100000 plus Smaller Cities plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.050\n",
      "Model:                            OLS   Adj. R-squared:                  0.027\n",
      "Method:                 Least Squares   F-statistic:                     2.201\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.145\n",
      "Time:                        22:29:43   Log-Likelihood:                -149.06\n",
      "No. Observations:                  44   AIC:                             302.1\n",
      "Df Residuals:                      42   BIC:                             305.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      4.6169      5.228      0.883      0.382      -5.933      15.167\n",
      "total_tb       4.7495      3.201      1.484      0.145      -1.711      11.210\n",
      "==============================================================================\n",
      "Omnibus:                       14.669   Durbin-Watson:                   2.569\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               15.876\n",
      "Skew:                           1.298   Prob(JB):                     0.000357\n",
      "Kurtosis:                       4.385   Cond. No.                         10.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_all).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_all.total_tb, df_all.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities over 100000 Plus Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 2.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do that with some logs too!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND ALL CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             diff_votes   R-squared:                       0.013\n",
      "Model:                            OLS   Adj. R-squared:                 -0.011\n",
      "Method:                 Least Squares   F-statistic:                    0.5352\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.469\n",
      "Time:                        22:29:43   Log-Likelihood:                 78.551\n",
      "No. Observations:                  42   AIC:                            -153.1\n",
      "Df Residuals:                      40   BIC:                            -149.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept             0.1258      0.031      4.031      0.000       0.063       0.189\n",
      "log_diff_total_tb    -0.0043      0.006     -0.732      0.469      -0.016       0.008\n",
      "==============================================================================\n",
      "Omnibus:                        0.947   Durbin-Watson:                   1.970\n",
      "Prob(Omnibus):                  0.623   Jarque-Bera (JB):                0.258\n",
      "Skew:                           0.013   Prob(JB):                        0.879\n",
      "Kurtosis:                       3.383   Cond. No.                         28.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"diff_votes ~ log_diff_total_tb\", data = df_all).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND ALL CITIES:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_all.log_diff_total_tb, df_all.diff_votes)\n",
    "plt.xlabel(\"LN of (Total TB Mortality 1918 - Total TB Mortality 1914)\")\n",
    "plt.ylabel(\"Difference in Votes\")\n",
    "plt.title(\"LN First Difference Total TB Mortality vs Difference in Votes for ALL CITIES\")\n",
    "plt.plot([3, 8], [reg.params[0] + reg.params[1] * 3, reg.params[0] + reg.params[1] * 8])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND ALL CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             diff_votes   R-squared:                       0.018\n",
      "Model:                            OLS   Adj. R-squared:                 -0.032\n",
      "Method:                 Least Squares   F-statistic:                    0.3630\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.698\n",
      "Time:                        22:29:43   Log-Likelihood:                 78.659\n",
      "No. Observations:                  42   AIC:                            -151.3\n",
      "Df Residuals:                      39   BIC:                            -146.1\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept             0.1441      0.052      2.795      0.008       0.040       0.248\n",
      "log_diff_total_tb    -0.0036      0.006     -0.572      0.571      -0.016       0.009\n",
      "log_population       -0.0019      0.004     -0.449      0.656      -0.010       0.007\n",
      "==============================================================================\n",
      "Omnibus:                        0.652   Durbin-Watson:                   1.986\n",
      "Prob(Omnibus):                  0.722   Jarque-Bera (JB):                0.142\n",
      "Skew:                          -0.095   Prob(JB):                        0.931\n",
      "Kurtosis:                       3.212   Cond. No.                         113.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"diff_votes ~ log_diff_total_tb + log_population\", data = df_all).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND ALL CITIES:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND ALL CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         prop_vote_1930   R-squared:                       0.126\n",
      "Model:                            OLS   Adj. R-squared:                  0.060\n",
      "Method:                 Least Squares   F-statistic:                     1.917\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.142\n",
      "Time:                        22:29:43   Log-Likelihood:                 83.453\n",
      "No. Observations:                  44   AIC:                            -158.9\n",
      "Df Residuals:                      40   BIC:                            -151.8\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept          0.1127      0.032      3.487      0.001       0.047       0.178\n",
      "prop_vote_1928     0.8236      0.392      2.103      0.042       0.032       1.615\n",
      "total_tb          -0.0013      0.018     -0.075      0.941      -0.038       0.035\n",
      "population     -1.915e-08   2.44e-08     -0.785      0.437   -6.85e-08    3.02e-08\n",
      "==============================================================================\n",
      "Omnibus:                        1.482   Durbin-Watson:                   1.958\n",
      "Prob(Omnibus):                  0.477   Jarque-Bera (JB):                0.647\n",
      "Skew:                          -0.017   Prob(JB):                        0.724\n",
      "Kurtosis:                       3.593   Cond. No.                     2.30e+07\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.3e+07. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"prop_vote_1930 ~ prop_vote_1928 + total_tb + population\", data = df_all).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND ALL CITIES:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND ALL CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         prop_vote_1930   R-squared:                       0.129\n",
      "Model:                            OLS   Adj. R-squared:                  0.087\n",
      "Method:                 Least Squares   F-statistic:                     3.036\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):             0.0589\n",
      "Time:                        22:29:43   Log-Likelihood:                 83.537\n",
      "No. Observations:                  44   AIC:                            -161.1\n",
      "Df Residuals:                      41   BIC:                            -155.7\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept              0.1074      0.008     13.203      0.000       0.091       0.124\n",
      "prop_vote_1928         0.7952      0.359      2.217      0.032       0.071       1.519\n",
      "normalized_diff_tb -5.516e-07   6.17e-07     -0.895      0.376    -1.8e-06    6.94e-07\n",
      "==============================================================================\n",
      "Omnibus:                        2.104   Durbin-Watson:                   1.953\n",
      "Prob(Omnibus):                  0.349   Jarque-Bera (JB):                1.212\n",
      "Skew:                           0.098   Prob(JB):                        0.546\n",
      "Kurtosis:                       3.789   Cond. No.                     5.95e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.95e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"prop_vote_1930 ~ prop_vote_1928 + normalized_diff_tb\", data = df_all).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND ALL CITIES:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "MULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND ALL CITIES:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         prop_vote_1930   R-squared:                       0.025\n",
      "Model:                            OLS   Adj. R-squared:                  0.001\n",
      "Method:                 Least Squares   F-statistic:                     1.057\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.310\n",
      "Time:                        22:29:43   Log-Likelihood:                 81.045\n",
      "No. Observations:                  44   AIC:                            -158.1\n",
      "Df Residuals:                      42   BIC:                            -154.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept              0.1201      0.006     19.916      0.000       0.108       0.132\n",
      "normalized_diff_tb -6.605e-07   6.43e-07     -1.028      0.310   -1.96e-06    6.36e-07\n",
      "==============================================================================\n",
      "Omnibus:                        1.586   Durbin-Watson:                   1.821\n",
      "Prob(Omnibus):                  0.453   Jarque-Bera (JB):                0.729\n",
      "Skew:                          -0.160   Prob(JB):                        0.695\n",
      "Kurtosis:                       3.543   Cond. No.                     9.56e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 9.56e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"prop_vote_1930 ~ normalized_diff_tb\", data = df_all).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND ALL CITIES:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total TB Mortality and Smaller Cities plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.009\n",
      "Model:                            OLS   Adj. R-squared:                 -0.057\n",
      "Method:                 Least Squares   F-statistic:                    0.1413\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):              0.712\n",
      "Time:                        22:29:43   Log-Likelihood:                -59.208\n",
      "No. Observations:                  17   AIC:                             122.4\n",
      "Df Residuals:                      15   BIC:                             124.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     10.0418      7.959      1.262      0.226      -6.923      27.007\n",
      "total_tb       1.8469      4.912      0.376      0.712      -8.624      12.317\n",
      "==============================================================================\n",
      "Omnibus:                        3.767   Durbin-Watson:                   2.676\n",
      "Prob(Omnibus):                  0.152   Jarque-Bera (JB):                2.117\n",
      "Skew:                           0.857   Prob(JB):                        0.347\n",
      "Kurtosis:                       3.231   Cond. No.                         8.64\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Brandon\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_small_grouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_small_grouped.total_tb, df_small_grouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 2.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total TB Mortality and Cities over 100000 plus Grouped Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.109\n",
      "Model:                            OLS   Adj. R-squared:                  0.084\n",
      "Method:                 Least Squares   F-statistic:                     4.411\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):             0.0428\n",
      "Time:                        22:29:43   Log-Likelihood:                -127.63\n",
      "No. Observations:                  38   AIC:                             259.3\n",
      "Df Residuals:                      36   BIC:                             262.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.6105      6.048     -0.101      0.920     -12.876      11.654\n",
      "total_tb       7.7923      3.710      2.100      0.043       0.268      15.317\n",
      "==============================================================================\n",
      "Omnibus:                       12.999   Durbin-Watson:                   2.370\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               13.068\n",
      "Skew:                           1.267   Prob(JB):                      0.00145\n",
      "Kurtosis:                       4.354   Cond. No.                         11.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3cAAAJdCAYAAACRc47yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABwC0lEQVR4nO3deXhTZd7/8U/atKWle1rKvjSVRSAFVKSIVlRAhbIURdBB1HHUEfWZ+c3oIC744I6OG+6OCyrjiAqIC+6PqCPuMy0gIA37Vtp035vk/P4oZOxAaVnSpKfv13V5XfakOfm2d0Ly6X2f720xDMMQAAAAAKBNCwl0AQAAAACAY0e4AwAAAAATINwBAAAAgAkQ7gAAAADABAh3AAAAAGAChDsAAAAAMAHCHYDjrl+/fsrKytKkSZM0efJkjRs3TlOnTtWaNWuave8bb7yhxYsXS5Jee+01Pfvss0f02DNnzlS/fv20Y8eORse//fZb9evXT88///wRnU+SrrjiChUVFTU6lpeXp0mTJmnSpEk688wzddJJJ/m+fumll7R06VLfsYkTJ+r888/X1VdfrX379h3x47eE0+nU9ddfr6ysLE2cOFG/+c1v9MMPP0iSdu7cqaFDh/rlcY9GZWWlhg0bpn//+98H3XbNNdfopZdeavK+O3bs0PXXX3/calm6dKnOPPNM/fa3vz2m8+Tn52vOnDm+3/+FF16oTz75xHf7pEmTVFZWpvLycl166aUHHT9eXnvtNd/zbfz48brxxhu1e/fu43b+pmzYsEGjRo1qdCwnJ0dTp07Veeedp1mzZjV67j/zzDM699xzNWbMGC1cuFAHdmUqKirSlVdeqfPPP18TJkzQTz/95LvP559/rqysLI0bN0433HCDKioqJEkej0d3332373yvvfbaIWtcuHChRowY4ft3KSsrS5dddpm2bNkiqeHfjg8++OC4/U6+//57XXnllRo3bpzOPfdcTZ48WW+//fZxO//xNHToUO3cuTPQZQA4HgwAOM769u1ruFyuRsf+9re/GdOmTWv2vn/5y1+Mv/3tb0f92L/5zW+MM88801i4cGGj43PmzDFGjhx5VOc+1M/za2+99ZZx1VVXNXts3rx5xi233HLEj98cp9NpnHbaacYXX3zhO/b1118bJ510kvHLL78YO3bsMIYMGXLcH/dY3HHHHQf9Lvbs2WMMHTrUKC0tbfJ+33zzjTF+/PjjVsfMmTON5cuXH9M5XC6XceaZZxrLli0zvF6vYRiGsX79emPEiBHGV1991eh7/TkW9913n3HZZZcZu3fvNgzDMDwej7Fs2TJj1KhRxp49e/zymPX19caLL75ojBw5stHPVVtba5xxxhnGDz/8YBiGYSxevNi48sorDcMwjM8//9yYNGmSUVlZadTU1BiXXHKJ8d577xmGYRg33HCD8dRTTxmGYRg///yzMWrUKKOqqspwuVzGiBEjjC1bthiGYRgLFiww5s2bZxiGYbz66qvGlVdeadTX1xslJSXGuHHjjJycnINqfeyxx4z//d//bXTs5ZdfNqZMmWIYRsO/HStXrjwuv5fPP//cOOOMM4zvv//ed2zHjh3G2LFjjQ8//PC4PMbxNGTIEGPHjh2BLgPAccDMHQC/c7vd2rNnj+Li4iRJhYWFuvbaa3XRRRfprLPO0syZM+VyufTxxx/rs88+00svvaTFixdr4cKFmj9/viRp06ZNmjlzpm9mZPny5U0+3sSJE/XOO+/4vq6urtZPP/2kjIwM37Gmzvftt99q4sSJmj59urKysnTzzTdLkmbNmqU9e/Yc9e+gvr5eFRUVSk5OPui2hx56SHfeeafv61WrVunCCy+U2+3WvHnzlJWVpezsbN1www2qrKw86P7PPfecpk6dqtNPP913LCMjQ3/961/VoUMHSQ2zG7fffrumTJmic845Rx9++KGkpsdCks466ywtXLhQF198sUaPHq1HHnnEd/5nn31WY8eO1ZQpU3T33XfrrLPOkiTV1dXpnnvu0ZQpUzRx4kTNmTPHN8Pya5dccolWrlypqqoq37E333xT48ePV2xsrD755BNNnjxZEydO1IwZM5SbmyuPx6Nbb71V27dv9820/fTTT7r44os1ZcoUTZ06Vf/3f/8nSSooKNAVV1yhKVOmaMqUKY1qP+Cee+7RmjVr9Oijj+qll15SeXm5/vznP2vChAnKysrSggUL5Ha7JUmDBg3S//zP/2jcuHEHzUD//e9/17BhwzR58mRZLBZJUv/+/fXYY48pKSlJUsNsdlFRkW6++WbV1NRo0qRJ8ng8vuNSw6x1dna2Jk+erMsuu0xOp1OS9MMPP+iCCy5Qdna2srOzfWP3a3v37tU//vEPPfLII+rSpYskKSQkxDdz/swzz+irr75SVlaW7z5lZWU65ZRTVFpaqvz8fM2ePVvZ2dnKysrS008/Lalh1jczM1NXXHGFxo0bd9DM888//6yNGzfq8ccfb3R8zZo1io6O1kknnSRJuuCCC7R69WoVFxfr448/1oQJExQVFaWIiAhlZ2drxYoVcrvd+vzzzzVt2jRJ0oABA9S7d299+eWX+uqrrzR48GD17t1bkjRjxgy98847MgxDn3zyibKzs2W1WhUXF6fx48drxYoVB/2ODiUjI8M3c3fAf890//rrljyvJOnBBx/UzTffrJNPPtl3rHv37rr77rt9r8k5c+bommuu0fjx4/XAAw8c9vn36+fJr7/+9ttvdeGFF+p//ud/lJWVpQsvvND3vDnca/GHH37wzWDedttt8nq9Lfp9AQh+hDsAfjFr1ixlZWVp1KhRGjdunCTp3nvvlSS99957GjJkiF5//XV9+umn6tChg95++22NGTNGZ511li677DJdcsklvnO53W79/ve/18yZM/XOO+/oueee00MPPaR//etfh3zsAQMGKDw8XDk5OZKkjz76SGeddZasVmuLzrdp0yb99a9/1TvvvOOredGiRb4PzS114APUxIkTNWrUKH333Xe64IILDvq+Cy+8UO+9957q6uokScuWLdO0adP073//W999951WrFihpUuXqkePHtq4ceNB91+7dq2GDRt20PHMzEz16NFDklRbW6vTTjtNy5Yt01/+8hc98MADkpoeiwOqqqr097//Xf/4xz/0wgsvaMeOHfryyy+1dOlSvfnmm1q6dGmjwPnss88qNDRUS5cu1YoVK9SpUyc9+OCDB9WWlpamE0880bcMzuv16q233tIll1wip9OpefPmaeHChVqxYoVuuOEGXXvttaqurtZdd92lnj176vnnn1dpaaluvvlmLViwQMuWLdOTTz6pO+64Q7t379aSJUvUvXt3LVu2TIsXL9a2bdtUXl7eqIa5c+dq0KBBuummm3TZZZfprrvuUnx8vN555x299dZb2rhxo1544QVJDeF89OjR+vDDDzV48OAW/f5POeUU9evXr9Gxe++91/c7Dg0N9R3/7rvvtHz5ci1evFjLly/XlVdeqeuuu05Sw3LCyy+/XEuXLtU999yjb7755qDHysnJUWpqqu8PKL82cuRI/fjjjzrttNNUWVnpC6fvvvuuMjMzFRcXpxtvvFFTp071jevXX3+t999/X1JDcLz22mv14YcfqlOnTo3O7XA4dO+99x70R4u9e/eqc+fOvq/Dw8OVmJio/Px87dmzp9FrqXPnzsrPz1dxcbG8Xq8SExN9t6WkpGjv3r0Hna9z586qqKhQZWXlIc+3d+/eg34P/83tduvNN9/Uqaee2uz3HtCS51VZWZl++eWXg5apStLJJ5+sM844w/d1TU2N3nvvPd14442Hff4dztq1a33/lmVnZ+vGG2+U1PRrsa6uTv/zP/+jOXPmaPny5Tr11FNVU1PT4t8BgOBmDXQBAMxp0aJFSkxM1Lp163TVVVfp1FNPlc1mk9QQ/H744Qe9+OKL2rp1qzZt2qT09PQmz7V161bV1tZq7Nixkho+8I0dO1Zffvllk9eSTZo0SStWrFB6erqWL1+um2++2fdB6XDnO/XUU9WlSxd169btmH8HJ598sp555hlJDeHlqaee0pVXXqn333/fN8MjST169FC/fv302WefKSMjQ998843uvvtueTwehYaG6sILL/SFZIfDcdDjWCyWZv/yHhYW5gvZ/fv3983ONTcWZ599tqSG35HNZlNpaalWrVqlc889V7GxsZIaZuEOBI7PP/9c5eXl+vrrryU1hKID4/7fLr74Yr366qvKzs7WF198oS5duqh///5avHixRowY4QumGRkZSkxM1Nq1axv93v7973+roKBAs2fPbvS72Lhxo04//XRdddVV2rNnj0aOHKk//elPiomJOezv6IsvvtBrr70mi8Wi8PBwTZ8+XYsWLdJVV10lSY1mYX7NYrH4rhk7Wp9//rm2bdum6dOn+46VlZWppKRE5513nubPn6/PPvtMI0eO1P/7f//vkOc4MMvz3+rq6mSxWGSxWDR16lQtW7ZMgwcP1tKlS3XTTTepqqpK33//vUpLS/Xoo49Kagj1GzZskMPhkNVq1ZAhQ47o5/F6vY3GSpIMw1BoaKgMw2h0m2EYCgkJOex9DnWb1DA72dT5DuX999/Xjz/+KKnhuTlw4MBGs+bNacnz6sBz4dc1/eEPf9CWLVt8r4dXXnlFknwzm1Lzz7+m9O/f3/fcnDp1qubPn6/i4uImX4u//PKLrFarbyXDhAkTdPvtt7f4dwAguBHuAPjVwIEDdfPNN2vOnDkaMGCAunfvrgceeEC5ubmaOnWqTj31VLnd7sN+OPZ4PIf80NfUh1lJysrK0tSpU3XZZZepoqJCffv2bfH5oqKijuZHPayQkBDNnDlTjz32mFwul2+53gHTpk3T8uXL5XK5dM4556hjx46SpLfffls//fSTvvnmG/3hD3/Qb3/720azmpI0ZMgQ/fvf/9bo0aMbHX/88cfVs2dPDRs2TGFhYb7jv/7ZmxuLiIiIRvczDENWq7XR9/x6Bsrr9Wru3LnKzMyU1NA8pba29pC/kzFjxuiee+7R1q1btWTJEt/P1dSHfLfb3ejn8Hg8stvteuONN3zH8vPzlZiYqLCwMH366adavXq1vvnmG1144YV67rnnNGjQoEPWcqjH9Xq9jZ5jTT0vDvz+f/Ob3zQ6/o9//EPV1dW6/PLLm3zMXz/WpEmTfLMuXq9X+/btU1xcnKZPn67Ro0frn//8p7788ks9/vjj+uCDDxqNzZAhQ7Rt2zYVFBQcNIv27bff+v4IcsEFF2jKlCm68MILVV5eruHDh6uiokKGYegf//iHIiMjJTU0NomIiFBxcbHCw8N9s94t1aVLl0ZLOOvr61VSUqKUlJSDbtu3b586d+4sm80mwzBUUlKi+Ph4320pKSmKjo72zcRLDeMcFxenqKioJs93KOeff36zQea/w3p9fb3v/x0OR7PPq7i4ONntdn333Xe+1+SB5ZvffvttozD56+dUc8+/Aw7M8B/w69ffr4819VrcvXv3Qf/eHun4AgheLMsE4HcTJkzwLd+SpK+++kqzZs3S5MmTZbPZ9PXXX8vj8Uhq+FDy3x9oUlNTZbVa9dFHH0lq+GD34YcfauTIkU0+ZkpKivr166e5c+dq0qRJx3S+Q9V0ND7//HN169at0bKzA8aMGaN169ZpyZIlvmuO/u///k+XXXaZhg4dquuvv16TJ0/W2rVrD7rvb3/7W73xxhv66quvfMe++OILvfLKK+rfv/9hazrcWDQlMzNTH330kW852ptvvum7bdSoUVq8eLHq6urk9Xp122236aGHHjrkeaxWq6ZNm6aXX35ZP//8s28mNSMjQ1999ZWv4+nq1au1Z88epaenKzQ01Pdh+0Cg+f777yVJ69ev17hx45Sfn68HH3xQTz75pM455xzdcsstSktL06ZNmw77c40aNUqvvvqqDMNQXV2dlixZctjn2AEXXXSRb/nsgQ/Na9eu1WOPPdbojwoHfmaPx3PQh+tRo0bpvffe84WU1157TbNmzZIkTZ8+XevXr1d2drbuvPNOlZWVqaCgoNH9U1JSNHPmTP2///f/lJ+f7zv+1ltv6aOPPtLvfvc73/c5HA7dfvvtviXC0dHRGjJkiF588UVJDTOGM2bM0Kefftrsz96U9PR0lZSU+LpdvvXWWxoyZIhiY2N19tlna8WKFaqqqlJdXZ2WLl2qc845R1arVWeeeaaWLFkiqaEDp9Pp1KmnnqpRo0YpJydHW7duldQQnA/MKp999tl666235Ha7VVZWpvfee0/nnHPOUdceGxur+vp65eXlSWpYunxAS59Xc+bM0V133dWo22dFRYU+//zzJmcVD/f8S0xMbLSc9tc2bNigDRs2SJJef/11DR06VLGxsU2+Fvv16yfDMLRq1SpJ0qeffqrS0tKj/XUBCDL8qQZAq7jttts0ceJEffnll5o9e7YWLFigRx99VGFhYRo2bJi2b98uSTrjjDN03333NbpvWFiYnnzySd11111auHChPB6PZs+erREjRhz2MSdNmqS5c+dq4cKFLT7ft99+e9B5zj33XM2cOVMLFy486MP64Ry45s5iscjtdis+Pl5PPPHEIT/chYeH6/zzz9fXX3/tW3p5xhln6IsvvvA1n4iLizvkErJevXrp6aef1iOPPKL777/fd93SU089pb59+x62xfnhxqIpGRkZmjZtmi666CJ16NBBJ5xwgm/G59prr9X999+vKVOmyOPxaMCAAZozZ06T55o2bZrOPvtsXXXVVb5ZubS0NM2bN0/XXXedPB6POnTooKeffloxMTFKS0tTRESELrjgAr3xxht67LHHtGDBAtXW1sowDC1YsEDdu3fXrFmzNGfOHE2YMEHh4eHq16+fxo8ff9if69Zbb9Vdd92lrKws1dfX6/TTT9c111xz2PtIUnx8vF555RU98MADeuaZZxQSEqLIyEjdfffdOu200xp9b3JyshwOh8aPH+/b8kNq+GD/u9/9TldccYUsFouio6P1+OOPy2Kx6M9//rPuuecePfLII7JYLLruuuvUvXv3g+r405/+pDfeeEO///3vVVdXp7q6Og0ePFj/+Mc/Gi0zPtCA46mnnvIde/DBB3XnnXcqKytLdXV1mjBhgiZOnHjU7fHDwsL0+OOPa/78+aqurlZ8fLzuv/9+SQ2Nen755RddeOGFqq+v19lnn63JkydLkubNm6dbb71VEyZMkMVi0YIFC3zLHu+9917dcMMNqq+vV8+ePX3nmzFjhrZv365Jkyapvr5eF110kYYPH35UdUtSTEyMbrzxRv3ud79TYmKizj33XN9tLX1enXHGGXrooYf01FNPadeuXaqvr5dhGDrjjDN8S7X/2+Gef7feeqvmz5+v2NhYjRw5stHsbFJSkh555BHt2rVLiYmJWrBggaSmX4thYWF64okndMcdd+ihhx7SgAEDmlw6DaDtsRjHeqEAAKBdWbNmjf71r3/59mt78cUXlZOT02TnQAD+cWCZ53/P5gFov5i5AwAckT59+ui5557TkiVLZLFY1KVLlyNqSgEAAPyDmTsAAAAAMAEaqgAAAACACRDuAAAAAMAECHcAAAAAYAKEOwAAAAAwgTbXLbO4uFJeLz1gAslmi5bLVRHoMrAf4xFcGI/gwngEH8YkuDAewYXxCC7BOB4hIRYlJHRs8vY2F+68XoNwFwQYg+DCeAQXxiO4MB7BhzEJLoxHcGE8gktbGw+WZQIAAACACRDuAAAAAMAECHcAAAAAYAKEOwAAAAAwAcIdAAAAAJgA4Q4AAAAATIBwBwAAAAAmQLgDAAAAABMg3AEAAACACRDuAAAAAMAECHcAAAAAYAKEOwAAAAAwAcIdAAAAAJgA4Q4AAAAATIBwBwAAAAAmYPXnyR999FF9+OGHslgsuuCCC3T55Zfr5ptv1o8//qjIyEhJ0nXXXacxY8b4swwAAAAAMD2/hbvvvvtO33zzjVasWCG3263zzz9fmZmZWrt2rV599VV16tTJXw8NAAAAAO2O35ZlDh8+XC+//LKsVqtcLpc8Ho86dOig3bt3a+7cucrKytJjjz0mr9frrxIAAAAAoN2wGIZh+PMBHnvsMb3wwgs699xzde211+r+++/XvHnzFBMTo6uvvloTJkzQtGnT/FkCAAAAcFx8/uMOvbxyvQqLq5WUEKlLzxugM0/qEeiyAEmtEO4kqbq6Wtdcc43OP/98XXTRRb7jH3/8sZYvX64nnniixedyuSrk9fq9ZBxGcnKMCgrKA10G9mM8ggvjEVwYj+DDmAQXxuPIrF63V4tWblCd+z8rz8KtIZp1Xn9lDOx8zOdnPIJLMI5HSIhFNlt007f764GdTqfWr18vSYqMjNTYsWP1/vvv68MPP/R9j2EYslr92tMFAAAAOC6WrnI2CnaSVOf2aukqZ4AqAhrzW7jbuXOnbr31VtXV1amurk6ffvqpTjnlFN1zzz0qLS1VfX29Xn/9dTplAgAAoE1wldUe0XGgtflt2iwzM1O5ubmaPHmyQkNDNXbsWF133XVKSEjQjBkz5Ha7NXbsWE2YMMFfJQAAAADHjS024pBBzhYbEYBqgIO1yjV3xxPX3AVeMK4/bs8Yj+DCeAQXxiP4MCbBhfE4Mlxz174E43g0d80dF7wBAAAALXAgwC1d5ZSrrFa22AhlZ9qPS7ADjgfCHQAAANBCGQM7E+YQtPzWUAUAAAAA0HoIdwAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAE7AGugAAABBYq9ft1dJVTrnKamWLjVB2pl0ZAzsHuiwAwBEi3AEA0I6tXrdXi1ZuUJ3bK0lyldVq0coNkkTAA4A2hmWZAAC0Y0tXOX3B7oA6t1dLVzkDVBEA4GgR7gAAaMdcZbVHdBwAELwIdwAAtGO22IgjOg4ACF6EOwAA2rHsTLvCrY0/DoRbQ5SdaQ9QRQCAo0VDFQAA2rEDTVPolgkAbR/hDgCAdi5jYGfCHACYAMsyAQAAAMAECHcAAAAAYAKEOwAAAAAwAcIdAAAAAJgA4Q4AAAAATIBwBwAAAAAmQLgDAAAAABMg3AEAAACACRDuAAAAAMAECHcAAAAAYAKEOwAAAAAwAcIdAAAAAJgA4Q4AAAAATIBwBwAAAAAmQLgDAAAAABMg3AEAAACACRDuAAAAAMAECHcAAAAAYAKEOwAAAAAwAcIdAAAAAJgA4Q4AAAAATIBwBwAAAAAmQLgDAAAAABMg3AEAAACACRDuAAAAAMAE/BruHn30UZ1//vkaP368XnzxRUnS119/raysLI0dO1YPP/ywPx8eAAAAANoNq79O/N133+mbb77RihUr5Ha7df755ysjI0Nz587VK6+8oi5duujqq6/WqlWrlJmZ6a8yAAAAAKBd8NvM3fDhw/Xyyy/LarXK5XLJ4/GorKxMvXr1Uo8ePWS1WpWVlaUPPvjAXyUAAAAAQLvh12WZYWFheuyxxzR+/HhlZGRo3759Sk5O9t3eqVMn5efn+7MEAAAAAGgX/LYs84AbbrhBv/vd73TNNddo69atslgsvtsMw2j0dUvYbNHHu0QcheTkmECXgF9hPIIL4xFcGI/gw5gEF8YjuDAewaWtjYffwp3T6VRdXZ0GDBigyMhIjR07Vh988IFCQ0N931NQUKBOnTod0Xldrgp5vcbxLhdHIDk5RgUF5YEuA/sxHsGF8QgujEfwYUyCC+MRXBiP4BKM4xESYjnsZJfflmXu3LlTt956q+rq6lRXV6dPP/1U06dP15YtW7Rt2zZ5PB69++67OuOMM/xVAgAAAAC0G36bucvMzFRubq4mT56s0NBQjR07VuPHj1diYqKuv/561dbWKjMzU+eee66/SgAAAACAdsNiGEabWuPIsszAC8Yp6vaM8QgujEdwYTyCD2MSXBiP4MJ4BJdgHI+ALcsEAAAAALQewh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAtZAFwAA8J/V6/Zq6SqnXGW1ssVGKDvTroyBnQNdFgAA8APCHQCY1Op1e7Vo5QbVub2SJFdZrRat3CBJBDwAAEyIZZkAYFJLVzl9we6AOrdXS1c5A1QRAADwJ8IdAJiUq6z2iI4DAIC2jXAHACZli404ouMAAKBtI9wBgEllZ9oVbm38z3y4NUTZmfYAVQQAAPyJhioAYFIHmqbQLRMAgPaBcAcAJpYxsDNhDgCAdoJlmQAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAoQ7AAAAADABwh0AAAAAmIDVnyd//PHHtXLlSklSZmambrrpJt1888368ccfFRkZKUm67rrrNGbMGH+WAQAAAACm57dw9/XXX+urr77SsmXLZLFYdOWVV+rjjz/W2rVr9eqrr6pTp07+emgAAAAAaHf8tiwzOTlZc+bMUXh4uMLCwmS327V7927t3r1bc+fOVVZWlh577DF5vV5/lQAAAAAA7YbFMAzD3w+ydetWzZgxQ4sXL9Zf//pXzZs3TzExMbr66qs1YcIETZs2zd8lAAAAAICp+T3cbdq0SVdffbWuv/56TZkypdFtH3/8sZYvX64nnniixedzuSrk9fo9j+IwkpNjVFBQHugysB/jEVwYj+DCeAQfxiS4MB7BhfEILsE4HiEhFtls0U3f7s8H//HHH3XZZZfpT3/6k6ZMmaKNGzfqww8/9N1uGIasVr/2dAEAAACAdsFv4W7Pnj2aPXu2HnzwQY0fP15SQ5i75557VFpaqvr6er3++ut0ygQAAACA48Bv02bPP/+8amtrdd999/mOTZ8+XVdddZVmzJght9utsWPHasKECf4qAQAAAADajVZpqHI8cc1d4AXj+uP2jPEILoxHcGE8gg9jElwYj+DCeASXYByPgF5zBwAAAABoHYQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAEyDcAQAAAIAJEO4AAAAAwAQIdwAAAABgAoQ7AAAAADABwh0AAAAAmADhDgAAAABMgHAHAAAAACZAuAMAAAAAE2g23DmdTr3xxhsyDEN/+MMfdM455+ibb75pjdoAAAAAAC3UbLibN2+eIiIi9Pnnnys/P1933323Hn744daoDQAAAADQQs2Gu9raWk2cOFFfffWVzjvvPJ166qmqr69vjdoAAAAAAC3UbLirq6tTYWGhPv/8c40cOVKFhYWqra1tjdoAAAAAAC3UbLi76KKLNHr0aJ100klKS0vTBRdcoFmzZrVGbQAAAACAFrI29w0XX3yxpk+frpCQhhy4bNkyJSQk+L0wAAAAAEDLNTtzV1lZqbvuukuzZs1SSUmJHn74YVVWVrZGbQAAAACAFmo23N11112KiYmRy+VSRESEKioqdPvtt7dGbQAAAACAFmo23K1fv15//OMfZbVaFRkZqQcffFDr169vjdoAAAAAAC3UbLg7cK3dAR6P56BjAAAAAIDAarahyimnnKIHHnhANTU1+vLLL7V48WINHz68NWoDAAAAALRQs1Nwf/7znxUVFaWYmBg9/PDD6tevn+bMmdMatQEAAAAAWqjZmbtVq1Zp9uzZmj17tu/Y8uXLNXnyZH/WBQAAAAA4Ak2Gu88++0xut1sLFiyQYRgyDEOS5Ha7tXDhQsIdAAAAAASRJsPd+vXr9c0338jlcunll1/+zx2sVl122WWtURsAAAAAoIWaDHcHlmIuXrxYl1xySWvWBAAAAAA4Qs1eczd9+nQ999xz+uKLL+R2u3XaaafpmmuukdXa7F0BAAAAAK2k2W6ZDz/8sL755hvNmjVLl19+uf71r39pwYIFrVEbAAAAAKCFmp1+++KLL/TWW28pLCxMknTmmWdq4sSJmjt3rt+LAwAAAAC0TLMzd4Zh+IKdJIWHhzf6GgAAAAAQeM2Gu/79++uee+7R9u3btWPHDt17773q27dva9QGAAAAAGihJsNdfX29JGnevHkqLS3V9OnTNW3aNBUVFem2225rtQIBAAAAAM1r8pq7zMxMTZs2TTNmzND999/fmjUBAAAAAI5QkzN3zz33nIqKipSVlaXrr79e3377bWvWBQAAAAA4Ak2Gu4EDB2r+/Pn6/PPPdfrpp+uBBx7Q+PHjtXjxYlVWVrZmjQAAAACAZjTbUCUqKkrTpk3Tm2++qb/+9a/auHGjMjMzW6M2AAAAAEALNbvP3QHffvut3njjDX3zzTeaOHGiP2sCAAAAAByhw4a7/Px8LV26VG+99ZYiIyM1ffp0/e///q86duzYWvUBAAAAAFqgyXB35ZVX6vvvv9dZZ52le+65R8OHD2/NugAAAAAAR6DJcDdkyBDde++9Sk5Obs16AAAAAABHoclwd91117VmHQAAAACAY9Bst0wAAAAAQPAj3AEAAACACbRoK4S6ujpVV1fLMAzfsfj4eH/VBAAAAAA4Qs2Gu9dee0333nuv6uvrJUmGYchisWj9+vV+Lw4AAAAA0DLNhrvnn39er732mgYOHNga9QAAAAAAjkKz19wlJSUR7AAAAAAgyDUb7kaNGqW///3vys/PV0lJie8/AAAAAEDwaHZZ5rPPPqu6ujrNnz/fd4xr7gAAAAAguDQb7nJzc1ujDgAAAADAMWgy3L399tuaNGmSXnzxxUPefvnll/utKAAAAADAkWky3G3btk2S9Msvv7RaMQAAAACAo9NkuLvhhhskSffee2+rFQMAAAAAODrNdssEAAAAAAQ/v4a7xx9/XOPHj9f48eO1YMECSdLXX3+trKwsjR07Vg8//LA/Hx4AAAAA2g2/hbuvv/5aX331lZYtW6bly5dr3bp1evfddzV37lw9+eSTev/997V27VqtWrXKXyUAAAAAQLvRbLjzer3629/+pr/85S+qqKjQM888I4/H0+yJk5OTNWfOHIWHhyssLEx2u11bt25Vr1691KNHD1mtVmVlZemDDz44Lj8IAAAAALRnze5zt2DBAhUVFWnNmjWSpC+//FIFBQW69dZbD3u/E044wff/W7du1cqVK/Wb3/xGycnJvuOdOnVSfn7+ERVss0Uf0ffDP5KTYwJdAn6F8QgujEdwYTyCD2MSXBiP4MJ4BJe2Nh7NhrvVq1dr2bJlys7OVnR0tF544QVNmjSpxQ+wadMmXX311brpppsUGhqqrVu3+m4zDEMWi+WICna5KuT1Gkd0HxxfyckxKigoD3QZ2I/xCC6MR3BhPIIPYxJcGI/gwngEl2Acj5AQy2Enu5pdlmm1WhUS8p9vCw8Pl9XabCaUJP3444+67LLL9Kc//UlTpkxR586dVVBQ4Lu9oKBAnTp1atG5AAAAAABNazal9e3bV4sXL5bH49HmzZv10ksvqX///s2eeM+ePZo9e7YefvhhZWRkSJLS09O1ZcsWbdu2Td27d9e7776rqVOnHvtPAQAAAADtXLPh7pZbbtE999wjl8ulGTNmaNSoUc1ebydJzz//vGpra3Xffff5jk2fPl333Xefrr/+etXW1iozM1Pnnnvusf0EAAAAAABZDMNoUxewcc1d4AXj+uP2jPEILmYcj9Xr9mrpKqdcZbWyxUYoO9OujIGdA11Wi5hxPNo6xiS4MB7BhfEILsE4Hs1dc9fszN3MmTMbNT2xWCyKjIzUCSecoKuvvlrR0XSvBACzWr1urxat3KA6t1eS5Cqr1aKVGySpzQQ8AADai2YbqqSlpSksLEwzZ87UrFmzFBMTo6ioKNXU1OiOO+5ohRIBAIGydJXTF+wOqHN7tXSVM0AVAQCApjQ7c5ebm6vXX3/d1yEzMzNTF198sR566CFNmDDB7wUCAALHVVZ7RMcBAEDgNDtzV15erl9fluf1elVVVdVw55Bm7w4AaMNssRFHdBwAAAROszN3o0eP1hVXXKHJkyfLMAytWLFCZ555plasWKGkpKTWqBEAECDZmfZG19xJUrg1RNmZ9gBWBQAADqXZcPeXv/xFS5Ys0aeffiqr1apJkyYpOztbX3/9te69997WqBEAECAHmqa01W6ZAAC0J82Gu5CQEGVnZ+u8887zLc8sLS3Vaaed5vfiAACBlzGwM2EOAIA2oNlw99prr+nee+9VfX29JMkwDFksFq1fv97vxQEAAAAAWqbZcPf888/rtdde08CBA1ujHgAAAADAUWi23WVSUhLBDgAAAACCXLPhbtSoUfr73/+u/Px8lZSU+P4DAAAAALPxeL1y7i5VcXlNoEs5Ys0uy3z22WdVV1en+fPn+45xzR0AAAAAs6iorteazS7lOl1au9mlyhq3JmfaNTGjV6BLOyLNhrvc3NzWqAMAAASp1ev2sh0GAFMxDEM7CyqV6yxUTp5Lzt2lMgwpNipMQ9KS5EhL0tkjequspCrQpR6RZsNdXV2dVq1apcrKSkmSx+PR9u3b9cc//tHvxQEAgMBavW5vo43sXWW1WrRygyQR8AC0KbX1Hq3fWtwQ6JwuFZfXSpJ6dY5R1sjectiT1LtLjEIsFklSRFhoIMs9Ks2Guz/+8Y/asWOHCgoKdOKJJyonJ0fDhw9vjdoAAECALV3l9AW7A+rcXi1d5STcAQh6BSXVynW6lOMs1IZtJXJ7vIoID9Wg3olyjLJpsN2m+OiIQJd53DQb7tavX6+PPvpId9xxhy6//HJ5vV7dcccdrVAaAAD+wTLDlnOV1R7RcQAIJLfHK+euUuU4XcrJK9QeV8OyypSESI0e2k3paTb17REva2izfSXbpGbDXadOnWS1WtW7d2/98ssvOu+881ReXt4atQEAcNyxzPDI2GIjDhnkbLHm+Us3gLatrKpOa5z7m6FsKVJ1rVuhIRb16xmvzCHdlG63KSUxKtBltopmw11UVJTeeecd9e/fX0uWLFFqaqqqqtrWhYUAABzAMsMjk51pbxSGJSncGqLsTHsAqwLQnhmGoe35FcpxFirX6dKW3WUyJMVFh+vkfsly2JN0Yu8ERUY0G3VMp9mf+Pbbb9eSJUt044036s0339RvfvMbmqkAANoslhkemQOBl2WsAAKpps6tn7cWKyevULmbXSqtqJNFUu8usZp0eh+l25PUIyXa1wylvWo23PXu3Vs33XSTJOmRRx7xdz0AAPgVywyPXMbAzoQ5AK0uv7hKuXku5ToLtXFHidweQ5ERoRrYx6Z0u02DU22K7Rge6DKDSrPh7scff9Tjjz8ul8slwzB8x9955x2/FgYAgD+YbZkhzWEAmIXb49UvO0r2d7d0Kb+o4VKwLrYonXNSDznsNqV1jzNtM5Tjodlwd9ttt2natGkaMGCALO18mhMA0PaZaZkhzWEAtHWlFbXK3d8MZd3WItXUeWQNDVH/nvE656TuGmy3qVN8ZKDLbDOaDXfh4eG67LLLWqEUAABah1mWGdIcBkBb4zUMbdtbrpy8ho3Et+1t6MKfEBOhU09MkcNu04m9EhUR3vY2EA8GzYa71NRUrVmzRoMHD26NegAAQAvRHAZAW1Bd69a6LUXKcRZqzeYilVXWyWKR7F3jlH1Gqhx2m3p0imaV4HHQZLjLysqSJFVWVmrGjBnq0aOHrNb/fDvX3AEAEFg0hwEQjAzD0N6iKuXsb4ayaWepPF5DURFWDbbb5NjfDCU6MizQpZpOk+Hutttua806AADAETJbcxgAbVe926uNO4p9ga6gpEaS1C25o8YO76F0e5Ls3WIVGkIzFH9qMtwNHz5cxcXF8nq9stlskqTVq1erX79+SkxMbLUCAQDAoZmpOQyAtqe4vFa5+zcS/3lrsWrrPQqzhmhArwSdO7ynBtttSoqjGUprajLcbdq0STNnztSdd96pMWPGSJI+/vhj3XjjjXr55ZeVmpraakUCAIBDM0tzGADBz+s1tHlPWUOgy3Np+74KSZIttoNGDu6sdLtN/XsmKDyMZiiB0mS4++tf/6pbbrnFF+wk6fbbb9egQYP0wAMP6KmnnmqVAgEAAAAERlVNvdZuKVJOXkMzlIrqeoVYLErrFqsLzrQr3W5T16SONEMJEk2Gu927d/uaqvxadna2XnjhBb8WBQAAAKD1GYah3YWVvo3E83aWymsYio4M0+DURDnsSRqUmqiOHWiGEoyaDHehoU1Pp4aFMZgAAACAGdTVe7Rhe7FynC7l5rnkKmtohtKzU7TOz+gphz1JqV1iFRLC7FywazLc2Ww2rV+/XgMGDGh0/Oeff1ZkJBdGAgAAAG2Vq7RGuc6GjcQ3bCtWndur8LAQDeydqAkje8lhT1JCDNuqtDVNhrtrr71W1157rWbPnq2hQ4fKMAz961//0pNPPqm77rqrNWsEAAAAcAw8Xq+cu8r2L7cs1K6CSklScnwHnZ7eVel2m/r1jFeYlWYobVmT4W7YsGFasGCBFi5cqHvuuUchISEaMmSIHnjgAZ188smtWSMAAACAI1RRXa81m13Kdbq0drNLlTVuhYZYdEL3OE0bnab0NJs6J0bRDMVEmgx3knTKKafo5Zdfbq1aAAAAABwlwzC0Y1+Fcp0Ngc65u1SGIcVGhWnICUlKtyfpxN6Jiupw2AiANoyRBQAAANqo2jqPft5W5At0xeW1kqRenWOUNbK3HPYk9e4SoxBm59oFwh0AAADQhhSUVPuunduwrURuj1cR4aEa1DtRjlE2DbbbFB9NM5T2iHAHAAAABDG3x6u8naW+QLfHVSVJSkmM0lnDuslht6lvj3hZQ0MCXCkCrUXhbteuXSotLZVhGL5jAwcO9FtRAAAAQHtWVlmnNZsbNhJft6VI1bUNzVD69YxX5pBuSrfblJIYFegyEWSaDXePPvqoXnjhBdlsNt8xi8WiTz/91K+FAQAAAO2FYRjK21miVT9sV67TpS27y2RIiosO18n9kuWwJ+nE3gmKjGDhHZrW7LPj7bff1kcffaSUlJTWqAcAAABoF6pr3fp5a7FynYXK3exSaUWdLJL6dI3VpNP7KN2epB4p0TRDQYs1G+66dOlCsAMAAACOg/yiKuU4Xcp1Fmrj9hJ5vIYiI0I1sI9Npw/tpl5JHRXbMTzQZaKNajbcZWRkaMGCBTr77LPVoUMH33GuuQMAAAAOz+3x6pcdJQ3NUPIKlV9cLUnqYovSmJN7yGG3Ka17nKyhIUpOjlFBQXmAK0Zb1my4W7p0qSTpgw8+8B3jmjsAAADg0Eoran37zq3bWqSaOo+soSHq3yte5+wPdMnxkYEuEybUbLj77LPPWqMOAAAAoE3yGoa27ilXrrNQOU6Xtu1tmH1LiInQiBNT5LAnaUCvBEWEhwa4Uphds+GuqqpKCxYs0BdffCG3263TTjtNt9xyi6Kjo1ujPgAAACDoVNW4tW5rkXLzCrVms0tlVfWyWCR71zhln5Eqh92mHp2iZaEZClpRs+Hu3nvvlcfj0RNPPCGPx6O///3vuvPOO3X//fe3Rn0AAABAwBmGob1FVcrJa2iGsmlnqTxeQx07WDUo1SaH3abBqTZFR4YFulS0Y82Gu5ycHK1YscL39V133aXx48f7tSgAAAAg0OrdHm3cXuLrbllQUiNJ6pbcUeOG95TDbpO9W6xCQ0ICXCnQoNlw5/F45PV6FbL/Sev1ehUaynphAAAAmE9xea1ynIXKzXPp521Fqqv3Ktwaov69EnTu8J5y2JNki+vQ/ImAAGjRVgh/+MMfNGPGDEnSa6+9plNPPdXvhQEAAAD+5vUa2rynrGEj8TyXtu+rkCTZYjvotMFdlG63qX/PBIWHMbmB4NdsuJszZ46efPJJPfTQQ/J4PDr99NN17bXXtkZtAAAAwHFXWVOvtZuLlOss1JrNRaqorleIxaK07nG68Ey7HHabuiZ1pBkK2pxmw53VatUNN9ygG264oTXqAQAA7czqdXu1dJVTrrJa2WIjlJ1pV8bAzoEuCyZiGIZ2FVY27D2XV6i8XWXyGoaiI8M0ODVRDnuSBqUmqmMHmqGgbWsy3M2YMUOvvfaahg4desi/Wvz0009+LQwAAJjf6nV7tWjlBtW5vZIkV1mtFq3cIEkEPByTunqPNmwvbmiGkueSq6yhGUrPTtE6P6Ph2rnULrEKCWF2DubRZLh79NFHJUnvvvvuQbcZhuG/igAAQLuxdJXTF+wOqHN7tXSVk3CHI+YqrfFtJL5hW7Hq3F5FhIXqxN4JmjCylxz2JCXERAS6TMBvmgx3nTp1kiTNmzdPf/vb3xrdNm3aNC1ZssS/lQEAANNzldUe0XHg1zxer5y7yhq6Wzpd2lVQKUlKju+gM9K7ypFmU78eCQqzslUB2ocmw90NN9ygLVu2aMeOHcrKyvIdd7vdCg8Pb5XiAACAudliIw4Z5GyxzK7g0Mqr6rR2c5FynIVat6VIlTVuhYZYdEL3OE0bnab0NJs6J0bRDAXtUpPh7qabbtKuXbt022236bbbbvMdDw0NVVpaWqsUBwAAzC07097omjtJCreGKDvTHsCqEEwMw9COfRUNzVCcLjl3l8owpNioMA05IUnp9iSd2DtRUR2a7RMImF6Tr4Lu3bure/fuGjx4sIYPH96aNQEAgHbiwHV1dMvEr9XWefTztiJfoCsub5jd7d05Rlkjeys9LUm9OscohNk5oJFm/8SxadMmGYbB1DYAAPCLjIGdCXPQvpJq5eY1XDu3YXuJ3B6vOoSHamDvRDlG2TTYblN8NMt1gcNpNtwlJydr/PjxSk9PV8eOHX3Hb731Vr8WBgAAAPNye7zK21mqXKdLOc5C7XFVSZJSEqN01rBuctht6tsjXtZQmqEALdVsuBs6dKiGDh3aGrUAAADAxMoq67Rms0s5TpfWbSlSdW1DM5T+PeN15pBucqTZlJIQFegygTar2XB33XXXqbKyUuvWrZPb7ZbD4VB0dHRr1AYAAIA2zGsY2p5frty8hkC3dU+ZDElx0eE6uV+yHPYkndg7QZERNEMBjodmX0m5ubm69tprlZSUJI/Ho/z8fD399NMaNmxYa9QHAACANqS61q2ftxYr11mo3M0ulVbUySKpT9dYTTq9j9LtSeqZEk0/B8APmg13999/vx588EGNGDFCkrR69Wrdd999bGIOAAAASVJ+UZVynC7lOgu1cXuJPF5DkRFWDeqTKIfdpsGpNsV2ZJ9kwN+aDXeVlZW+YCdJGRkZuueee/xaFAAAAIKX2+PVxh0lys1rCHT5xdWSpC62KI05pYfS7TbZu8XRDAVoZc2GO4vFol27dqlbt26SpJ07dyo0NNTvhQEAACB4lFTU+vadW7e1SLV1HllDQ9S/V7zOObmHHHabkuMjA10m0K41G+5mz56tiy66SBkZGZKkf/7zn5o3b16LTl5RUaHp06fr6aefVvfu3XXzzTfrxx9/VGRkwwv/uuuu05gxY46hfAAAAPiD1zC0dU+5cp2FynG6tG1vuSQpISZCGSemyGFP0oBeCYoI54/+QLBoNtydc845Sk1N1TfffCPDMHTNNdfIbrc3e+KcnBzdeuut2rp1q+/Y2rVr9eqrr6pTp07HVDQAADC/1ev2aukqp1xltbLFRig7085m535WVePWuq1Fys0r1JrNLpVV1ctikezd4jQ1M1UOe5K6J3ekGQoQpFrUd3bHjh3avHmzQkNDlZaW1qJwt2TJEs2bN0833XSTJKm6ulq7d+/W3LlzlZ+frzFjxui6665TSAhrsQEAQGOr1+3VopUbVOf2SpJcZbVatHKDJBHwjiPDMLS7sHL/cstCbdpZKo/XUMcOVg1KtfmaoURHhgW6VAAt0Gy4W7hwod5//32de+658nq9uv3223XJJZfo0ksvPez97r777kZfFxYWasSIEZo3b55iYmJ09dVX680339S0adOO7ScAAACms3SV0xfsDqhze7V0lZNwd4zq3R5t3F7SsJH41iLtdVVJkrond9S44T3lsNtk7xarUP4AD7Q5FsMwjMN9w5gxY7R06VLFxMRIkkpLSzV9+nStXLmyRQ9w1lln6eWXX1b37t0bHf/444+1fPlyPfHEE0dZOgAAMKuJf3pbh/qAYpG04q+TWrucNq+wpFo/rM/XD+vz9e9NBaqt8yg8LFTpJyTplAEpOmlAijolRAW6TADHqNmZu/j4eHXs2NH3dWxsrKKijvzFv3HjRm3dulXjxo2T1LAMwGpt0arQRlyuCnm9h82j8LPk5BgVFJQHugzsx3gEF8YjuDAewaelY5IYGyFXWe0hjzOmzfN6DW3eXaYcZ6FynS7t2FchSbLFdtDIQZ2Vbrepf88Edesa3/D7dHv4vQYB/s0KLsE4HiEhFtls0U3e3my6Oumkk3TttdfqoosuUmhoqFasWKGuXbvqo48+kiSNHTu2RYUYhqF77rlHI0aMUFRUlF5//XVNmTKlhT8GAABoT7Iz7Y2uuZOkcGuIsjObv+6/vaqsqdfazUXKdRZqzeYiVVTXK8RiUVr3OF14pl0Ou01dk2iGAphZs+Fu3bp1kqQXXnih0fFXXnlFFoulxeGuf//+uuqqqzRjxgy53W6NHTtWEyZMOIqSAQCA2R24ro5umU0zDEO7DjRDyStU3q4yeQ1D0ZFhGpxqU3qaTQP7JKpjB5qhAO1Fs9fcHeB2u2UYhsLCAvsPBMsyAy8Yp6jbM8YjuDAewYXxCD6MybGpq/do/bZiX3fLA0tXe3aKliPNJoc9SaldYhUS0rLZOcYjuDAewSUYx+OYl2W6XC795S9/0TfffCOPx6NTTjlFDzzwgFJSUo5roQAAADhYYWn1/jDn0vptxap3exURFqoTeydowsjectiTlBATEegyAQSBZsPd/PnzNWTIED300EPyeDx65ZVXdMcdd+ipp55qjfoAAADaFY/XK+eu/zRD2VVQKUnqFB+pzPSucqTZ1K9HgsKsbFUAoLFmw93WrVv16KOP+r6+4YYbNH78eL8WBQAA0J6UV9Vp7eYi5TgLtW5LkSpr3AoNseiE7nG66Kw0Oew2dU6MohkKgMNqNty53W7V1tYqIqJhur+6upp/WAAAAI6BYRjasa9COfuvndu8u0yGIcVGhWnICUlKtyfpxN6Jiupw5NtGAWi/mv0X4/zzz9dll12m7OxsWSwWvfXWW7696gAAANAytXUe/bytyHf9XHF5QzOU3p1jlDWyt9LTktSrc4xC+CM6gKPUbLibPXu2OnfurC+//FJer1fZ2dm64IILWqM2AACAoLF63d4j3pphX0m1cvMarp3bsL1Ebo9XHcJDNbBPohx2mxypNsVF0wwFwPHRbLibNWuWFi1apKlTp7ZGPQAAAEFn9bq9jTZVd5XVatHKDZLUKOC5PV5t2lmq3P3NUPa4qiRJKYlROmtYNznsNvXtES9rKM1QABx/zYa78vJyVVVVKSoqqjXqAQAAOGpHM7vWEktXOX3B7oA6t1dLVzk1sHei1mx2Kcfp0rotLlXXemQNtahfj3idOaSbHGk2pSTwOQqA/zUb7iIjIzV69Gj169evUcB7+umn/VoYAJiVvz58Au1dS2fXjsaBzcIPdfyPC7+SISkuOlyn9O8khz1JJ/ZOUIdwmqEAaF3N/qvD9XUAcPz488Mn0N4dbnbtWF9fiTHhKiqvO+i4NdSiCSN7K92epJ4p0XQUBxBQhw13v/zyizp27Kj09HSlpKS0Vk0AYFr+/PAJtHeHm107GvlFVcpxupSTV6iSioODXVioRZedP4DXLoCg0WS4e+utt3T//ferV69e2r59u/76179q1KhRrVkbAJjO8f7wCeA/bLERh3wt2WJb1o3S7fFq444S5eY17D2XX1wtSeqa1FFjh/eUNcSir9fuUVF5HUuqAQSlJsPdK6+8onfeeUcpKSn617/+pYcffphwBwDH6Fg/fAJoWnamvdGyZ0kKt4YoO9Pe5H1KKmp9+86t21qk2jqPrKEh6t8rXuec3EMOu03J8ZGNHgMAgtVhl2UeWIo5dOhQFRcXt0pBAGBmR/PhE0DLHJhFO1zDIq9haMuesv2zcy5tyy+XJCXERCjjxBQ57Eka0CtBEeGhAfkZAOBYNBnu/vuC4NBQ/pEDgGPVkg+fAI5exsDOB72eqmrcWre1qGEz8c0ulVfVy2KR7N3iNDUzVQ57krond6QZCoA2r8U9evkHDwCOj0N9+ARw/BiGoT2uqv3LLQu1aWepPF5DHTtYNTjVJofdpkGpNkVHhgW6VAA4rpoMdxs3btSwYcN8X9fU1GjYsGEyDEMWi0U//fRTqxQIAADQnHq3Rxu2NzRDyXEWqrC0RpLUPbmjxg3vKYfdJnu3WIWGhAS4UgDwnybD3ccff9yadQAAAByRorIa5W52KTfPpZ+3Famu3qtwa4gG9ErQeSN6yZFqky2uQ6DLBIBW02S469atW2vWAQAAcFher6HNu8uU4yxUrtOlHfsqJElJcR00anAXOexJ6t8zXuFh9AkA0D61+Jo7AACA1lZRXa+1Wxo6W67dXKSK6nqFWCxK6x6nC8+0y5GWpK62KHoDAIAIdwAAIIgYhqFdhZXKyWuYncvbVSrDkKIjwzQ41ab0NJsG9klUxw40QwGA/0a4AwCgCavX7WXbilZQW+/Rhm3Fvu6WrrJaSVLPlGiNz+itdLtNfbrEKiSE2TkAOBzCHQAAh7B63d5GG867ymq1aOUGSSLgHQeFpdX7w5xL67cVq97tVURYqE7snaAJI3vLYU9SQkxEoMsEgDaFcAcAwCEsXeX0BbsD6txeLV3lJNwdBY/Xq7ydpXrv2+36Zs0e7SqslCR1io9UZnpXOdJs6tcjQWFWtioAgKNFuAMA4BAOLA1s6XEcrLyqTms3FynHWai1m4tUVetWaIhFfXvEa5Sjixx2mzon0gwFAI4Xwh0AAIdgi404ZJCzxbJUsCmGYWjHvgrl7L92bvOuMhmSYqPCNKxvshx2mzJP6anK8ppAlwoApkS4AwDgELIz7Y2uuZOkcGuIsjPtAawq+NTWefTz1iLlOF1as9ml4vKGQNy7c4yyTuut9LQk9eoco5D9s3NRHcIIdwDgJ4Q7AAAO4cB1dXTLPNi+kmrl5hUqx+nSxu3FcnsMdQgP1cA+iXLYbXKk2hQXzQwnALQ2wh0AAE3IGNiZMCfJ7fFq085S5Tob9p7b46qSJHVOjNJZw7or3W7TCT3iZQ2lGQoABBLhDgAAHKS0sk5r9l87t25rkaprPbKGWtSvR7zOHNJNjjSbUhKiAl0mAOBXCHcAAEBew9D2/HLl5DUEui17yiVJ8dHhOqV/JznsSTqxd4I6hPPRAQCCFf9CAwDQTlXXuv/TDMXpUmllnSySUrvGasrpfeSwJ6lnSjRbFQBAG0G4AwCgHdlbVOVrhvLLjhJ5vIYiI6watL8ZymC7TbFR4YEuEwBwFAh3AACYWL3bq192lChnfzOUfcXVkqSuSR015pQeSrfbZO8WRzMUADABwh0AACZTUlGrXKdLuU6X1m0tUm2dR9bQEA3olaAxJzcEuqT4yECXCQA4zgh3AAC0cV7D0JY9ZcrNawh02/IbmqEkxEQo48QUOexJGtArQRHhoQGuFADgT4Q7AADaoKqaeq3dUqRcp0trNrtUXlUvi0Wyd4vT1MxUOexJ6p7ckWYoANCOEO4AAGgDDMPQHldVw7VzeS7l7SqVx2uoYwerBqfa5LDbNCjVpujIsECXCgAIEMIdAABBqt7t0YbtJcrNcynHWajC0hpJUvfkjjr31J5y2G1K7Rqr0BCaoQAACHcAALSa1ev2aukqp1xltbLFRig7066MgZ0bfU9RWY2vGcrP24pUV+9VuLWhGcp5I3rJkWqTLa5DgH4CAEAwI9wBANAKVq/bq0UrN6jO7ZUkucpqtWjlBnkNQynxUcpxFionz6WdBRWSpKS4Dho1uIsc9iT17xmv8DCaoQAADo9wBwAm05LZIbS+paucvmB3QJ3bqxfeWy/DkEIsFp3QPU4XjrbLYU9SV1sUzVAAAEeEcAcAJtLU7JAkAl4AGYYhV1ltE7dJ10waqEF9EhXVgWYoAICjR7gDABNpanZo6Son4a6V1dZ7tH5bccNWBc7CJr/PFhuh4QNSWrEyAIBZEe4AwESamh1q6jiOr8LSal8zlPXbilXv9ioiLFQn9k7Qib0T9c26var3GL7vD7eGKDvTHsCKAQBmQrgDABOxxUYcMsjZYiMCUI35ebxe5e0s9QW6XYWVkqRO8ZHKTO+q9LQk9e0RrzBrw1YF/XslcD0kAMBvCHcAYCLZmfZG19xJzA4db+VVdVqzuSHMrd1cpKpat0JDLOrbI16jHF3ksNvUOfHQzVAyBnYmzAEA/IZwBwAmciA4MDt0/BiGoR37KpSTV6hcp0ubd5fJkBTbMVzD+ibLYbdpYJ9ERUbwlgoACCzeiQDAZJgdOnY1dW6t31qsHKdLaza7VFzesNS1d+cYZZ3WW+lpSerVOUYhbFUAtAhbtACtg3AHAICkfcVVytl/7dzG7cVyewx1CA/VwD6JcthtcqTaFBfNtYvAkWKLFqD1EO4AAO2S2+PVpp2lvuWWe4uqJEmdE6N01rDuSrfbdEKPeFlDQwJcKdC2sUUL0HoIdwCAdqO0sk5rnC7lOgu1bmuRqms9soZa1K9ngkYP66Z0u02dEqICXSZgKmzRArQewh0AIGD8fR2O12toy56y/VsVFGrLnnJJUnx0uE7p30np9iQN6J2gDuG8HQL+whYtQOvh3QwAEBD+ug6nutatdVuKlOt0ad3WIhWX18oiKbVrrKac3kcOe5J6pkQfcqsCAMcfW7QArYdwBwAIiON5Hc7eoirl5hUqx+nSLztK5PEaioqw6qQBKerXPVaDUm2KjQo/nuUDaCG2aAFaD+EOABAQx3IdTr3bq192lCjH2dAMZV9xtSSpa1JHjT2lhxx2m9K6x6lzSpwKCsqPa90AjpyZtmhhWwcEM8IdACAgjvQ6nOLyWq3Z7FJOXqF+3las2jqPrKEhGtArQWNO7qF0u01J8ZH+LhtAO8a2Dgh2hDsAQEA0dx3OgWYoOU6XVq/dK1dZjSQpxCL165mgMaf00IBeCYoICw1I/QDaH7Z1QLAj3AEAAuJQ1+GMH9lboSEW/e3dn7Vms0vlVfX677YnXkNy7irVKEcXgh2AVsW2Dgh2hDsAQMCMODFFPVNilOssVG6eS69++Iu8hqGOHawanGqTw27TG/+Xp+KKukb34y/lAPzlcNfUsa0Dgh3hDgDQqurdHq3fVtIQ6JwuFZY2LLfsnhyt80b0lMNuk71rnEJCGubsnn3n50Oeh7+UAzjemrumjm0dEOwIdwAAvysqq9m/kbhLP28rUl29V+HWEJ3YO1Hnj+glh92mxNgOh7wvfyk/GN36AP9o7po6tnVAsCPcAQCOO6/XkHN3qXKdLuXkubSzoEKSlBTXQaMGd5HDnqT+PeMV3oJr5vhLeWN06wP8pyXX1JlpWweYD+EOAHBcVFTXa+3mhtm5NZtdqqxxK8Ri0Qnd43ThaLsc9iR1tUXJYvnvFimHx1/KG6NbH+A/rBRAW0e4AwAcFcMwtKug0reReN6uUhmGFBMVpvS0JDnsNg3qk6ioDmHH/Fj8pfw/6NbHslT4DysF0NYR7gAALVZb79H6bcX7r58rVNH+QNEzJVrjM3orPc2mPp1jfc1QcPy195kFlqXCn1gpgLaOcAcAOKzCkmrl7G+GsmF7serdXkWEherE3gmaeFofDU61KSGmfQSLYNDeZxZYlgp/Y6UA2jK/hruKigpNnz5dTz/9tLp3766vv/5a9957r2pra3Xeeefpj3/8oz8fHgBwFNwer5y7Sn2BbndhpSSpU0KkMod0Vbo9SX17xCvMGhLgStun9j6zwLJUAGia38JdTk6Obr31Vm3dulWSVFNTo7lz5+qVV15Rly5ddPXVV2vVqlXKzMz0VwkAgBYqr6rTmv3NUNZuLlJVrVuhIRb17RGvMxxd5EhLUufEqECXif3a88xCe1+WCgCH47dwt2TJEs2bN0833XSTJCk3N1e9evVSjx49JElZWVn64IMPCHcAEACGYWh7foVvI/HNu8tkSIrtGK5hfZPlsNs0sE+iIiNYvY/g0prLUmncAqCt8du79t13393o63379ik5Odn3dadOnZSfn3/E57XZoo+5Nhy75OSYQJeAX2E8gkuwjkd1rVv//qVAP6zP1w/r81VUViNJOqFHvGaM7aeTT0yRvVu86ZqhBOt4tGfHMiYTz4xRbEwHvbxyvQqLq5WUEKlLzxugM0/qcRwrlD7/cYde/mCjaus9khqWfb78wUbFxnQ47o8VaLxGggvjEVza2ni02p9kvV5vo72NDMM44r2OJMnlqpDXaxzP0nCEkpNjVFBQHugysB/jEVyCbTz2FVf5rp3buL1Ybo+hDuGhGtgnUZNH9dFgu01xHcN93+9yVQSw2uMv2MYDx2dMBvaM1/1XZzQ6drzH+aV31/mC3QG19R699O46DewZf1wfK5B4jQQXxiO4BON4hIRYDjvZ1WrhrnPnziooKPB9XVBQoE6dOrXWwwNAu+D2eLVpR4kv0O0tqpIkdU6M0lnDuivdbtMJPeJlDaUZCnA4NG4B0Ba1WrhLT0/Xli1btG3bNnXv3l3vvvuupk6d2loPDwCmVVpZ57t2bt2WItXUeWQNtahfzwSNHtZN6XabOiXQDAU4EjRuAdAWtVq4i4iI0H333afrr79etbW1yszM1LnnnttaDw8ApuE1DG3bW+7bSHzLnoYlI/HR4Ro+IEXpdpsG9E5Qh3CaoQBHq73vJwigbfL7O/9nn33m+/+MjAytWLHC3w8JAKZTXevWui1FDYFus0tllXWySErtGqspp/eRw56kninRR3UtM4CDtff9BAG0TfxZF4CptdVW5oZhaG9R1f7ZOZd+2VEij9dQVIRVg1IT5bDbNCjVptio8OZPBuCotOf9BAG0TYQ7AKa1et3eRsuqXGW1WrRygyQF5Qe2erdXG3cUKzevIdDtK6mWJHVL6qixp/SQw25TWvc4hYbQDAUAAByMcAfAtJaucja6XkaS6txeLV3lDJpwV1xeqzWbXcrJK9TPW4tVW+9RmDVEA3olaOzwHnKk2pQUHxnoMgEAQBtAuANgWsHYytzrNbRlT9n+rQoKtT2/YV+5xNgIZQzqLIfdpgG9EhQRFhqwGgEAQNtEuANgWsHSyryqpl5rtxQpJ8+lNZtdqqiul8UipXWL09TMVKXbk9QtuSPNUAAAwDEh3AEwrUC1MjcMQ7tdVQ17z+W5tGlnqbyGoY4drBpstzU0Q+ljU3RkmF/rAAAA7QvhDoBptWYr87p6jzZsL9GmL7fo27V7VFhaI0nqnhyt80b0lMNuk71rnEJCmJ0DAAD+QbgDYGr+bGVeVFbTcO1cXqHWbytWndur8LBQndgrQeeP6CWH3abE2A5+eWwAAID/RrgDgBbyeg05d5cq1+lSTp5LOwsamqEkxXXQ6Y6ucqTZNGpYD5WWVAW4UgAA0B4R7gDgMCqq67V2c8O+c2s2u1RZ41aIxaITusfpwtF2OexJ6mqL8jVDCafLJQA/WL1ub6ssMQfQthHuAOBXDMPQzoJK5ToLleN0ybmrVIYhxUSFKT0taX8zlERFdaAZSqDxYRftxep1exs1h3KV1WrRyg2SxHMeQCOEOwDtXm29R+u3Fjd0t9zsUtH+7RN6pcRoQkZvOdJs6tMlViFsVRA0+LCL9mTpKmejrr+SVOf2aukqJ893AI0Q7gC0S4Ul1fs3Endp/bZiuT1eRYSF6sTeCZp4Wh8NTrUpIaZ198NDy/FhF+3JofbrPNxxAO0X4Q5Au+D2eOXcVeoLdLsLKyVJnRIidebQrkq3J6lvj3iFWUMCXClagg+7aE9ssRGHfG7bYvkDFIDGCHcATKusqk5r9oe5tVuKVF3rVmiIRX17xOsMRxc50pLUOTEq0GVivyO5ho4Pu2hPsjPtjZYhS1K4NUTZmfYAVgUgGBHuAJiGYRjanl/RcO2c06XNu8tkSIrtGK6T+iUr3W7Tib0TFRnBP33B5kivoePDLtqTA68BGggBaA6fcAC0aTV1bv18oBmK06WSijpJUp8uMZo4qo8cdpt6dY6hGUqQO9Jr6Piwi/YmY2Bnnt8AmkW4A9Dm5BdXKTfPpVxnoTbuKJHbYygyIlQDeyfKYU/SYLtNcR3DA10mjsDRXEPHh10AABoj3AEIem6PV5t2lPiaoewtqpIkdbFF6eyTusthT9IJ3eNkDaUZSlvFNXQAABw7wh2AoFRaUavczQ1hbt2WItXUeWQNtahfzwSNHtZN6XabOiXQDMUsuIYOAIBjR7gDEBS8hqFte8uVk9dw7dzWveWSpPjocA0fkKJ0u00DeieoQ3hg/tk6kk6OOHJcQ4dgwusdQFtFuAMQMNW1bq3bUqQcZ6HWbC5SWWWdLJJSu8VqyhmpSrfb1KNTtCwBboZypJ0ccXS4hg7BgNc7gLaMcAeg1RiGob1FVcrdf+3cLztK5PEaioqwalBqohx2mwal2hQbFVzNUI60kyOAtovXO4C2jHAHwK/q3V5t3FG8v7ulS/tKqiVJ3ZI6auwpPeSw25TWPU6hIcHbDOVoOjkCaJt4vQNoywh3AI674vJa375zP28tVm29R2HWEA3olaCxw3vIkWpTUnxkoMtsMTo5Au0Hr3cAbRnhDsAx83oNbdlT1rBVQV6htu+rkCQlxkYoY1Bnpdtt6t8rQRFhoQGu9OjQyTF40fgCxxuvdwBtGeEOwFGpqqnX2i1Fyslzac1mlyqq62WxSGnd4jQ1M1Xp9iR1S+4Y8GYoxwOdHIMTjS/gD7zeAbRlhDsALWIYhnYXVirX6VKO06W8naXyGoY6drBqsN3W0Aylj03RkWGBLtUv6OQYfGh8AX/h9Q6grSLcAWhSXb1HG7aX+K6fKyytkST16BSt80b0VLo9SaldYxUS0vZn59D20PgCAIDGCHcAGikqq/FdO7d+W7Hq3F6Fh4XoxF6JOn9ELznsNiXGdgh0mQCNLwAA+C+EO6Cd83i9cu4q27/3XKF2FlRKkpLiOuh0R1c50mzq3zNeYda22QwF5kXjCwAAGiPcAe1QRXW91m5uuHZu7WaXKmvcCrFY1LdHnKaNTpPDblMXW5QpmqHAvGh8AQBAY4Q7oB0wDEM7CyqV6yxUjtMl565SGYYUExWm9LSk/c1QEhXVwZzNUGBeNL4AAOA/CHeASdXWe7R+a3FDM5TNLhXtvzapV0qMJmT0liPNpj5dYhXC7BwAAIApEO4AEykoqd6/VUGhNmwrkdvjVUR4qAb2TtTE02wanGpTQgzNJgAAAMyIcAe0YW6PV2uchfrixx3KySvUHleVJKlTQqTOHNpV6fYk9e0RrzBrSIArBQAAgL8R7oA2pqyqTmucLuU6XVq7pUjVtW6FhljUt0e8MtO7ypGWpM6JUYEuEwAAAK2McAcEOcMwtD2/Qjn7NxLfsrtMhqS4juE6qV+yTh/aXd0TIxUZwcsZAACgPePTIBCEaurc+nlrsXLyGpqhlFbUySKpd5dYTRrVR440m3qmxCjEYlFycowKCsoDXTIAAAACjHAHBIn84irl5jVsJL5xR4ncHkOREQ3NUBz2JA222xTXMTzQZQIAACBIEe6AAHF7vPplR8n+7pYu5Rc1NEPpYovS2Sd1l8OepBO6x8kaSjMUAAAANI9wB7Si0opa5W52KTfPpXVbi1RT55E11KL+PRN09rBucqQlqVN8ZKDLBAAAQBtEuAP8yGsY2ra3vOHaOadLW/c2XBuXEBOh4QNSlG63aUDvBHUI56UIAACAY8MnSuA4q651a92WIuU4C7Vmc5HKKhuaoaR2i9WUM1KVbrepR6doWSyWQJcKAGih1ev2aukqp1xltbLFRig7066MgZ0DXRYANEK4A46RYRjaW1SlnP3NUDbtLJXHaygqwqpBqYlKtydpUGqiYqJohgIAbdHqdXu1aOUG1bm9kiRXWa0WrdwgSQQ8AEGFcAcchXq3Vxt3FCs3z6UcZ6EKSmokSd2SOmrsKT3ksNuU1j1OoSE0QwGAtm7pKqcv2B1Q5/Zq6Son4Q5AUCHcAS1UXF6r3P0bif+8tVi19R6FWUM0oFeCxg3vKYfdpqQ4mqEAgNm4ymqP6DgABArhDmiC12to856yhkCX59L2fRWSJFtshEYO6iyH3ab+vRIUERYa4EoBAP5ki404ZJCzxUYEoBoAaBrhDviVqpp6rd1SpJy8hmYoFdX1sliktG5xuuBMuxx2m7oldaQZCgC0I9mZ9kbX3ElSuDVE2Zn2AFYFAAcj3KFdMwxDuwsrfRuJ5+0sldcw1LGDVYPtNjnsNg3qY1N0ZFigSwUABMiB6+rolgkg2BHu0O7U1Xu0YXuxcpwNm4m7yhqaofToFK3zRvRUuj1JqV1jFRLC7BwAoEHGwM6EOQBBj3CHdsFVWqPczS7l5BVqw7Zi1bm9Cg8L0Ym9EjV+ZC85Um1KjO0Q6DIBAACAo0a4gyl5vF45d5Up19mw99zOgkpJUlJcB53u6CpHmk39e8YrzEozFAAAAJgD4Q6mUVFdrzWbXcp1urR2s0uVNW6Fhlh0Qvc4TRudJofdpi62KJqhAAAAwJQId2izDMPQjn0V+2fnXHLuLpVhSDFRYRqSliRHWpIG9k5UVAee5gAAADA/PvWiTamt82j9tmLlOguV43SpuLxh36FeKTGakNFbjjSb+nSJVQizcwAAAGhnCHcIegUl1fu3KijUhm0lcnu8iggP1cDeiZo0qmG7gvhoNpIFAABA+0a4Q9Bxe7zK21nqC3R7XFWSpJSESI0e2k2ONJv6do9XmDUkwJUCAAAAwYNwh6BQVlmnNZsbNhJft6VI1bUNzVD69ohXZnpXOdKS1DkxKtBlAgAAAEGLcIeAMAxD2/MrlOMsVK7TpS27y2RIiusYrpP6JSvdbtOJvRMVGcFTFAAAAGgJPjmj1VTXuvXz1oZmKLmbXSqtqJNFUu8usZo0qo8caTb1TImhGQoAAABwFAh38Kv84irl5DVsJP7LjhK5PYYiI0I1sI9N6XabBqfaFNsxPNBlAgAAAG0e4Q7Hldvj1S87SvY3Q3Epv6ihGUoXW5TOPqm7HPYkndA9TtZQmqEAkrR63V4tXeWUq6xWttgIZWfalTGwc6DLAgAAbRDhDsestKLWt5H4uq1FqqnzyBpqUf+eCTp7WDc50pLUKT4y0GUCQWf1ur1atHKD6txeSZKrrFaLVm6QJAIeAAA4YoQ7HDGv19Dm3WW+jcS37S2XJCXEROjUE1PksNt0Yq9ERYSHBrhSILgtXeX0BbsD6txeLV3lJNwBAIAjRrhDi1TVuPXz1iLlOAu1bkuxSipqZZGU2i1WU85IVbrdph6domWhGQrQYq6y2iM6DgAAcDgBCXczZ85UUVGRrNaGh58/f77S09MDUQqaYBiG9hb9pxnKpp2l8ngNRUVYdfKAFPXrHqdBqYmKiaIZCnC0bLERhwxyttiIAFQDAADaulYPd4ZhaOvWrfq///s/X7hDcKh3e7Rxe4lynA2BrqCkRpLULbmjxg7voXR7kuzdYtU5JU4FBeUBrhZo+7Iz7Y2uuZOkcGuIsjPtAawKAAC0Va2erjZv3ixJuuKKK1RSUqJp06bpN7/5TWuXgf2Ky2sbNhLPc+nnbUWqq/cqzBqiAb0SNG54TznsNiXF0QwF8IcD19XRLbN9oUMqAMBfWj3clZWVKSMjQ7fddpvq6+t16aWXqk+fPjrttNNau5R2yes1tHlPQzOU3DyXtu+rkNSwDOy0QV3ksNvUv1eCIsJohgK0hoyBnflg347QIRUA4E8WwzCMQBbw0ksvaffu3Zo7d24gyzC1iqo6/bRxn75fn6+fNuxTWWWdQkIsGtA7UScPSNEpA1LUs3MMzVAAwM+uuOsjFRRXH3Q8OSFSL9w6NgAVAQDMpNVn7n744QfV19crIyNDUsM1eEdy7Z3LVSGvN6B5NOgZhqHdhZUN187lFSpvV5m8hqHoyDANSk2Uw27ToD42RUeG+e5TWFjR4vMnJ8dwzV0QYTyCC+MRXIJtPA4V7A4cD6Y6/SnYxqS9YzyCC+MRXIJxPEJCLLLZopu8vdXDXXl5uR577DH94x//UH19vZYtW6b//d//be0yTKeu3qMN24v3BzqXXGUNzVB6dIrWeSN6Kt2epNSusQoJYXYOAAKFDqkAAH9q9XA3evRo5eTkaPLkyfJ6vbr44os1dOjQ1i7DFFylNb6NxDdsK1ad26vwsBCd2CtR40f2kiPVpsTYDoEuEwCwHx1SAQD+FJC9CP7whz/oD3/4QyAeuk3zeL1y7ipr6G7pdGlXQaUkKTm+g05P76p0u039esYrzEozFAAIRnRIBQD4ExvNBbmK6nqt2exSrtOltZtdqqxxKzTEohO6x2na6DSlp9nUOTGKZigA0EbQIRUA4C+EuyBjGIZ27KtQrrMh0Dl3l8owpJioMA1JS5IjLUkDeycqqgNDBwAAAOA/SAhBoLbOo5+3FfkCXXF5w8X2vTrHKGtkbznsSerdJUYhzM4BAAAAaALhLkD2lVQrN6/h2rkN20vk9ngVER6qgb0TNXmUTYPtNsVH0z0NAAAAQMsQ7lqJ2+NV3s5S5TpdynEWao+rSpKUkhCp0UO7yZFmU9/u8QqzhgS4UgAAAABtEeHOj8oq67Rms0s5TpfWbSlSdW1DM5R+PeOVOaSb0u02pSRGBbpMAAAAACZAuDuOvIah7fnlys1rCHRb95TJkBTXMVwn9UtWuj1JJ/ZOUGQEv3YAAAAAxxcp4xjVuz3KdRYp11mo3M0ulVbUySKpd5dYTRrVR440m3qm0AwFAAAAgH8R7o7RW6s266PvdygyIlQD+9iUbrdpcKpNsR3DA10aAAAAgHaEcHeMxg3vqWF9k5XaNVbWUJqhAAAAAAgMwt0xSoiJUEIMWxYAAAAACCymmgAAAADABAh3AAAAAGAChDsAAAAAMAHCHQAAAACYAOEOAAAAAEyAcAcAAAAAJkC4AwAAAAATINwBAAAAgAkQ7gAAAADABAh3AAAAAGAChDsAAAAAMAHCHQAAAACYAOEOAAAAAEyAcAcAAAAAJkC4AwAAAAATINwBAAAAgAkQ7gAAAADABAh3AAAAAGAC1kAXAADBYPW6vVq6yilXWa1ssRHKzrQrY2DnQJcFAADQYoQ7AO3e6nV7tWjlBtW5vZIkV1mtFq3cIEkEPAAA0GYQ7mBKzMLgSCxd5fQFuwPq3F4tXeXkeQMAANoMwh1Mh1mYtiGYArirrPaIjgMAAAQjGqrAdA43C4PgcCCAHwhPBwL46nV7A1KPLTbiiI4DAAAEI2buYDrMwgS/YFsGmZ1pbzTbK0nh1hBlZ9pbvRYAwTWzDwBtCeEOpmOLjThkkGMWJngEWwA/8KGRD5NA4LG0HgCOHuEOpsMsTPALxgCeMbAzHxyBIBBsM/sA0JYQ7mA6zMIEPwL4wViGBjQItpl9AGhLCHcwJWZhghsBvDGWoQH/EYwz+wDQVhDuAAQEAfw/WIYG/Acz+wBw9Ah3ABBgLEMD/oOZfQA4eoQ7AAgwlqEBjTGzDwBHh03MASDAsjPtCrc2/ueYZWgAAOBIMXMHAAHGMjQAAHA8EO4AIAiwDA0AABwrlmUCAAAAgAkQ7gAAAADABAh3AAAAAGAChDsAAAAAMAHCHQAAAACYAOEOAAAAAEyAcAcAAAAAJkC4AwAAAAATINwBAAAAgAkQ7gAAAADABAh3AAAAAGAChDsAAAAAMAHCHQAAAACYAOEOAAAAAEyAcAcAAAAAJkC4AwAAAAATINwBAAAAgAkQ7gAAAADABKyBLuBIhYRYAl0CxDgEG8YjuDAewYXxCD6MSXBhPIIL4xFcgm08mqvHYhiG0Uq1AAAAAAD8hGWZAAAAAGAChDsAAAAAMAHCHQAAAACYAOEOAAAAAEyAcAcAAAAAJkC4AwAAAAATINwBAAAAgAkQ7gAAAADABAh3AAAAAGAChDs08s477+j888/X2LFjtXjx4ka3rV+/XpMmTfL9d/rpp2vChAmSpGXLlmnUqFG+2x5++OFAlG9KFRUVmjBhgnbu3HnQbevXr1d2drbGjRunW265RW63W5K0e/duXXLJJTr33HP1+9//XpWVla1dtmkdbjw++eQTTZo0SRMnTtS1116r0tJSSbw+/Olw4/H4449r9OjRvt/7gX/TeH34T1PjwftH63v88cc1fvx4jR8/XgsWLDjodt4/Wldz48H7R+trbkza7HuIAey3d+9eY/To0UZxcbFRWVlpZGVlGZs2bTrk91ZVVRnjx483vv/+e8MwDGP+/PnGO++805rltgv//ve/jQkTJhgDBw40duzYcdDt48ePN/71r38ZhmEYN998s7F48WLDMAzjqquuMt59913DMAzj8ccfNxYsWNBqNZvZ4cajvLzcOO2004y9e/cahmEYjzzyiHHnnXcahsHrw1+ae31cffXVxk8//XTQcV4f/tHceBzA+4f//fOf/zQuuugio7a21qirqzMuvfRS46OPPmr0Pbx/tJ7mxoP3j9bXktdIW30PYeYOPl9//bVGjBih+Ph4RUVFady4cfrggw8O+b3PPPOMTjnlFJ188smSpDVr1mjZsmXKysrSn//8Z99fnHBslixZonnz5qlTp04H3bZr1y7V1NRoyJAhkqTs7Gx98MEHqq+v1/fff69x48Y1Oo5jd7jxqK+v17x585SSkiJJ6tevn/bs2SOJ14e/HG48JGnt2rV65plnlJWVpfnz56u2tpbXhx81Nx4H8P7hf8nJyZozZ47Cw8MVFhYmu92u3bt3+27n/aN1NTcevH+0vubGRGq77yGEO/js27dPycnJvq87deqk/Pz8g76vvLxcS5Ys0XXXXec7lpycrGuvvVYrVqxQly5dNH/+/Fap2ezuvvtu3weg//bf45WcnKz8/HwVFxcrOjpaVqu10XEcu8ONR0JCgsaMGSNJqqmp0bPPPqtzzjlHEq8PfznceFRWVmrAgAG68cYbtWzZMpWVlenJJ5/k9eFHhxuPA3j/aB0nnHCCL7ht3bpVK1euVGZmpu923j9aV3PjwftH62tuTNryewjhDj5er1cWi8X3tWEYjb4+YMWKFTrnnHNks9l8x5544gmddNJJslgsuvLKK/Xll1+2Ss3tWVPjdahxO9Q4wj/Ky8t11VVXqX///poyZYokXh+B0LFjRz333HOy2+2yWq264oortGrVKl4fAcb7R+vatGmTrrjiCt10003q3bu37zjvH4HR1HgcwPtH62tqTNryewjhDj6dO3dWQUGB7+uCgoJDLq/55JNPdP755/u+Li8v10svveT72jAMhYaG+rVWHDxehYWF6tSpkxITE1VeXi6PxyOp6XHE8bdv3z5dfPHF6tevn+6++25JvD4CZffu3XrzzTd9XxuGIavVyusjwHj/aD0//vijLrvsMv3pT3/yBYUDeP9ofYcbD4n3j0A43Ji05fcQwh18Ro4cqdWrV6uoqEjV1dX66KOPdMYZZzT6HsMwtG7dOg0dOtR3LCoqSn/729+Uk5MjSXr11Vd9ywvgP926dVNERIR+/PFHSdLbb7+tM844Q2FhYTr55JP1/vvvS5KWL19+0Dji+PN4PLrmmmt03nnn6ZZbbvH9JY/XR2B06NBBDzzwgHbs2CHDMLR48WKNGTOG10cA8f7Revbs2aPZs2frwQcf1Pjx4w+6nfeP1tXcePD+0fqaG5O2/B5iDXQBCB4pKSn64x//qEsvvVT19fW64IIL5HA49Lvf/U433HCDBg8erKKiIoWFhSkiIsJ3v9DQUD3yyCO64447VFNTo969ex+ypSyOj1+Px4MPPqhbb71VFRUVGjhwoC699FJJ0rx58zRnzhw99dRT6tKlix566KEAV21eB8Zj7969+vnnn+XxePThhx9KkgYNGqS7776b10cr+vXrY/78+fr973+v+vp6DRs2TJdffrkkXh+tifePwHj++edVW1ur++67z3ds+vTp+uyzz3j/CIDmxoP3j9bXktdIW30PsRiGYQS6CAAAAADAsWFZJgAAAACYAOEOAAAAAEyAcAcAAAAAJkC4AwAAAAATINwBAAAAgAmwFQIAIKj169dPffv2VUhIiCwWi6qrqxUdHa077rhDgwcPPux933jjDdXV1emSSy7Ra6+9pvLycl111VUtfuyZM2fqu+++0yeffKIePXr4jn/77be69NJLddNNN+m3v/3tEf08V1xxhR588EElJib6juXl5elPf/qTJKm0tFTl5eXq3r27JGnKlCmKjY3V3Xffre7du8swDLndbvXo0UN33nln0G2gCwAIHMIdACDoLVq0qFEYev7553XXXXfp9ddfP+z9fvzxR51wwgmSpBkzZhzVY3ft2lVvv/22rrvuOt+x5cuXKykp6ajO989//vOgY2lpaXr77bclSUuXLtWHH36oZ555xnf70qVLdfLJJzc6dscdd+ixxx7TXXfddVR1AADMh3AHAGhT3G639uzZo7i4OElSYWGhbr/9drlcLhUUFKhbt2565JFH9NNPP+mzzz7TP//5T3Xo0EFFRUUqLi7W7bffrk2bNmn+/PkqKSmRxWLRFVdcocmTJx/y8SZOnKh33nnHF+6qq6v1008/KSMjw/c9TZ3v22+/1d13362oqChVVlZq0KBBkqRZs2bp2WefVZcuXY7qd1BfX6+KiopGs4kAABDuAABBb9asWZKk4uJiRUREaPTo0br33nslSe+9956GDBmiq666SoZh6KqrrtLbb7+tK664Qp9++qlOOOEEXXLJJVq4cKGkhnD4+9//XjfddJPGjh2r/Px8XXjhherVq5eGDh160GMPGDBAn332mXJycpSenq6PPvpIZ511loqLi5s9n9QQ/D755BN169ZNUsMs3H/PRLbEDz/8oEmTJskwDOXn5ysiIkJ//OMfj+4XCgAwJRqqAACC3qJFi/TOO+/omWeeUU1NjU499VTZbDZJDcFv2LBhevHFF3XHHXdo06ZNqqqqavJcW7duVW1trcaOHStJSklJ0dixY/Xll182eZ9JkyZpxYoVkhqWZE6ZMqXF5+vSpYsv2B2Lk08+WW+//bZWrFih1atX66KLLtKVV14pwzCO+dwAAHMg3AEA2oyBAwfq5ptv1pw5c7Rz505J0gMPPKBHH31UCQkJuuiii3TaaacdNvB4PB5ZLJZGxw40KWlKVlaWPvzwQ+3YsUMVFRXq27dvi88XFRV1xD9nc0JCQjRz5kxt3rxZLpfruJ8fANA2Ee4AAG3KhAkT5HA4fMsyv/rqK82aNUuTJ0+WzWbT119/LY/HI0kKDQ09KLSlpqbKarXqo48+kiTl5+frww8/1MiRI5t8zJSUFPXr109z587VpEmTjul8h6rpaHz++efq1q3bES/vBACYF9fcAQDanNtuu00TJ07Ul19+qdmzZ2vBggV69NFHFRYWpmHDhmn79u2SpDPOOEP33Xdfo/uGhYXpySef1F133aWFCxfK4/Fo9uzZGjFixGEfc9KkSZo7d67v2r2WnO/bb7896DznnnuuZs6cqYULFzaaAWzOgWvuLBaL3G634uPj9cQTTygkhL/TAgAaWAwW6wMAAABAm8ef+wAAAADABAh3AAAAAGAChDsAAAAAMAHCHQAAAACYAOEOAAAAAEyAcAcAAAAAJkC4AwAAAAATINwBAAAAgAn8f6OSFVntrW5uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_100000_grouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000_grouped.total_tb, df_100000_grouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities Over 100000 Plus Grouped\")\n",
    "plt.plot([.6, 2.5], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outlier Analysis for Total TB Mortality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see which points might be outliers/high leverage points, we will plot each points leverage against their studentized residual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Studentized Residuals')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_all).fit()\n",
    "influence = reg.get_influence()\n",
    "inf_sum = influence.summary_frame()\n",
    "\n",
    "student_resid = influence.resid_studentized_external\n",
    "(cooks, p) = influence.cooks_distance\n",
    "(dffits, p) = influence.dffits\n",
    "leverage = influence.hat_matrix_diag\n",
    "\n",
    "print ('\\n')\n",
    "plt.figure(figsize = (15, 10))\n",
    "sns.regplot(x = leverage, y = reg.resid_pearson,  fit_reg=False)\n",
    "plt.plot([.02, .17], [2, 2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([.02, .17], [-2, -2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/44, 4/44], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/44, 4/44], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.title('Leverage vs. Studentized Residuals- Bivariate Regression')\n",
    "plt.xlabel('Leverage')\n",
    "plt.ylabel('Studentized Residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Points past the 4/44 line are high leverage points, and points over 2 or under -2 studentized residuals are likely to be outliers. We will start by removing the points that are both outliers and high leverage points and see how the results change. These correspond with Bremerhaven and Muelheimruhr. We'll just get rid of Bremerhaven, first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.134\n",
      "Model:                            OLS   Adj. R-squared:                  0.113\n",
      "Method:                 Least Squares   F-statistic:                     6.497\n",
      "Date:                Tue, 16 Aug 2022   Prob (F-statistic):             0.0145\n",
      "Time:                        22:58:29   Log-Likelihood:                -145.56\n",
      "No. Observations:                  44   AIC:                             295.1\n",
      "Df Residuals:                      42   BIC:                             298.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -1.0839      5.119     -0.212      0.833     -11.415       9.247\n",
      "total_tb       7.9501      3.119      2.549      0.015       1.656      14.244\n",
      "==============================================================================\n",
      "Omnibus:                       16.273   Durbin-Watson:                   2.320\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               18.890\n",
      "Skew:                           1.334   Prob(JB):                     7.91e-05\n",
      "Kurtosis:                       4.784   Cond. No.                         11.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Studentized Residuals')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temp = df_all\n",
    "df_temp = df_temp[df_temp.city != \"BREMERHAVEN\"]\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_temp).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_temp.total_tb, df_temp.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities over 100000 Plus Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 2.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.6])\n",
    "plt.show()\n",
    "\n",
    "influence = reg.get_influence()\n",
    "inf_sum = influence.summary_frame()\n",
    "\n",
    "student_resid = influence.resid_studentized_external\n",
    "(cooks, p) = influence.cooks_distance\n",
    "(dffits, p) = influence.dffits\n",
    "leverage = influence.hat_matrix_diag\n",
    "\n",
    "print ('\\n')\n",
    "plt.figure(figsize = (15, 10))\n",
    "sns.regplot(x = leverage, y = reg.resid_pearson,  fit_reg=False)\n",
    "plt.plot([.02, .2], [2, 2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([.02, .2], [-2, -2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/43, 4/43], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/43, 4/43], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.title('Leverage vs. Studentized Residuals- Bivariate Regression')\n",
    "plt.xlabel('Leverage')\n",
    "plt.ylabel('Studentized Residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll get rid of both Bremerhaven and Muelheimruhr."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.069\n",
      "Model:                            OLS   Adj. R-squared:                  0.046\n",
      "Method:                 Least Squares   F-statistic:                     2.978\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):             0.0921\n",
      "Time:                        22:29:44   Log-Likelihood:                -134.96\n",
      "No. Observations:                  42   AIC:                             273.9\n",
      "Df Residuals:                      40   BIC:                             277.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      3.1262      4.844      0.645      0.522      -6.664      12.917\n",
      "total_tb       5.1241      2.969      1.726      0.092      -0.877      11.125\n",
      "==============================================================================\n",
      "Omnibus:                       16.967   Durbin-Watson:                   2.082\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.412\n",
      "Skew:                           1.305   Prob(JB):                     2.24e-05\n",
      "Kurtosis:                       5.329   Cond. No.                         11.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Studentized Residuals')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temp = df_all\n",
    "df_temp = df_temp[df_temp.city != \"BREMERHAVEN\"]\n",
    "df_temp = df_temp[df_temp.city != \"MUELHEIMRUHR\"]\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_temp).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_temp.total_tb, df_temp.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities over 100000 Plus Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 2.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.6])\n",
    "plt.show()\n",
    "\n",
    "influence = reg.get_influence()\n",
    "inf_sum = influence.summary_frame()\n",
    "\n",
    "student_resid = influence.resid_studentized_external\n",
    "(cooks, p) = influence.cooks_distance\n",
    "(dffits, p) = influence.dffits\n",
    "leverage = influence.hat_matrix_diag\n",
    "\n",
    "print ('\\n')\n",
    "plt.figure(figsize = (15, 10))\n",
    "sns.regplot(x = leverage, y = reg.resid_pearson,  fit_reg=False)\n",
    "plt.plot([.02, .2], [2, 2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([.02, .2], [-2, -2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/42, 4/42], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/42, 4/42], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.title('Leverage vs. Studentized Residuals- Bivariate Regression')\n",
    "plt.xlabel('Leverage')\n",
    "plt.ylabel('Studentized Residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, while the leverage points that are not outliers should likely be kept, we might want to consider removing the other two remaining outliers, as well. These correspond to Breslau and Hessen_50k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.151\n",
      "Model:                            OLS   Adj. R-squared:                  0.129\n",
      "Method:                 Least Squares   F-statistic:                     6.761\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):             0.0132\n",
      "Time:                        22:29:44   Log-Likelihood:                -116.30\n",
      "No. Observations:                  40   AIC:                             236.6\n",
      "Df Residuals:                      38   BIC:                             240.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.2036      3.615      0.333      0.741      -6.114       8.521\n",
      "total_tb       5.7431      2.209      2.600      0.013       1.272      10.214\n",
      "==============================================================================\n",
      "Omnibus:                        0.780   Durbin-Watson:                   1.988\n",
      "Prob(Omnibus):                  0.677   Jarque-Bera (JB):                0.853\n",
      "Skew:                           0.284   Prob(JB):                        0.653\n",
      "Kurtosis:                       2.565   Cond. No.                         11.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4AAAAJdCAYAAABj4xkSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAACBj0lEQVR4nOzdZ2BUZd7//08mjUAIJaRAQg0dpSQoIGCAUKVJEEQU21p2xeVe/7e62Ba7Lir2Xta6rigBy4rrEiCgoMKEGqoRkEkjCYH0MjPn/4AfcxshCSUzJ8m8X4+YM+2ba2YO85nzva7jYxiGIQAAAABAk2cxuwAAAAAAgGcQAAEAAADASxAAAQAAAMBLEAABAAAAwEsQAAEAAADASxAAAQAAAMBLEACBRqZXr16aOnWqpk+frssvv1wTJkzQzJkztWPHjjrv++mnn+qjjz6SJH388cd64403zuq5582bp169eunw4cPVtv/444/q1auX3n777bN6PEm68cYbdfTo0Wrbfv75Z02fPl3Tp0/XqFGjFBcX57r87rvvKikpybVt2rRpuuyyy3TrrbfqyJEjZ/38ZyI9PV1//vOfNXXqVE2bNk3XXHONNm/eLEmy2WwaNGiQW573XJSUlCg2NlZbt2495bo//vGPevfdd2u87+HDh/XnP/+53mpJSkrSqFGj9Ic//OG8HicnJ0cLFy50jf+sWbO0atUq1/XTp09XYWGhioqKdO21156yvTEqLCzU1KlTq32ujx49qptuukmXXXaZpkyZotTUVNd1a9eu1dSpUzVhwgQtWLBAxcXFkiSHw6HHHntMEydO1Lhx4/Txxx+77nPw4EFdffXVuuyyy3TFFVcoPT3ddd1nn32myy67TOPHj9eiRYtUVVV1So0//vij+vfv79oXTZ8+XYmJiVq9erUk6cUXX9TDDz9cL+NRVVWlxYsXu94DU6dO1Wuvvab6OpPVbz/H9VV3bfuNnJwczZkzR1L1z91vt58vT74+kpSRkaGFCxdqwoQJmjx5siZMmKBnn332tO8ds916661KSkoyuwzAPAaARqVnz55Gfn5+tW1vvfWWMXv27Drv+9e//tV46623zvm5r7nmGmPUqFHGiy++WG37woULjUsuueScHvt0f89vLVu2zLjlllvq3LZo0SLjvvvuO+vnr0t6eroxfPhwY926da5tGzZsMOLi4ox9+/YZhw8fNgYOHFjvz3s+HnzwwVPGIisryxg0aJBx/PjxGu/3ww8/GJMnT663OubNm2esWLHivB4jPz/fGDVqlLF8+XLD6XQahmEYu3fvNoYOHWp899131W7bEF+Lc7F27Vpj/PjxRr9+/Yzt27e7ti9YsMB49dVXDcMwjF27dhkjRowwSktLjfz8fGPo0KHGgQMHDMMwjMWLFxuLFi0yDMMwPvzwQ+Omm24yqqqqjGPHjhkTJkwwtm3bZhiGYcycOdP44osvXM85efJkw+l0Gnv37jUuvfRSIz8/33A4HMYdd9xhvPHGG6fUebr3y+7du42BAwca+fn5xgsvvGA89NBD9TImb775pvHnP//ZqKqqMgzDMAoLC40ZM2YY//rXv+rl8X/73qmPuuvab/xWfX/uantcd70+2dnZxvDhw41PPvnE9TktLi42br/9duORRx6pl+eoT7fccouxbNkys8sATMMRQKCRs9vtysrKUqtWrSRJeXl5uu2223TllVdqzJgxmjdvnvLz8/Xf//5Xq1ev1rvvvquPPvqo2q+/+/fv17x581y/VK9YsaLG55s2bZq+/PJL1+WysjKlpqZq2LBhrm01Pd6PP/6oadOmac6cOZo6daruueceSdJ1112nrKyscx6DqqoqFRcXKyws7JTrlixZokceecR1OSUlRbNmzZLdbteiRYs0depUJSYmasGCBSopKTnl/m+++aZmzpypkSNHurYNGzZMzzzzjJo1aybpxFGWv/3tb5oxY4bGjh2r//znP5Jqfi0kacyYMXrxxRc1d+5cjR49Ws8995zr8d944w2NHz9eM2bM0GOPPaYxY8ZIkiorK/X4449rxowZmjZtmhYuXOg60vNbV199tVauXKnS0lLXts8++0yTJ09WSEiIVq1apcsvv1zTpk3TVVddpe3bt8vhcOj+++/Xr7/+6jpil5qaqrlz52rGjBmaOXOm1qxZI0nKzc3VjTfeqBkzZmjGjBnVaj/p8ccf144dO/T888/r3XffVVFRke68805NmTJFU6dO1eLFi2W32yVJF1xwgf7nf/5HEyZMOOVI9j//+U/Fxsbq8ssvl4+PjySpd+/eeuGFF9SuXTtJJ46KHz16VPfcc4/Ky8s1ffp0ORwO13bpxNHvxMREXX755br++utdR7s2b96sK664QomJiUpMTHS9dr/3ySefaMqUKZo2bZpuvPFGHThwQEVFRYqNjVVubq7rdrNmzVJKSkqtr9WYMWP0l7/8RZMmTdJ///vfU57r/fff11NPPaXw8HDXNrvdrrVr12r27NmSpD59+qhLly5av369vvvuO1144YXq0qWLJOmqq67Sl19+KcMwtGrVKiUmJsrPz0+tWrXS5MmT9cUXXygnJ0e//PKLJk+eLEmKj49XaWmpdu3apeTkZI0ZM0Zt27aVxWLRlVdeqS+++OK04/J7vXv3VrNmzZSRkVFt+5gxY6q9ticvn+nnMDc3V1VVVaqsrJQktWzZUosXL3YdtZs3b56efPJJzZkzR+PHj9dbb72lJ598UomJiZo0aZL27t0rSdq6dauuvvpqzZo1S6NGjdK9995b69+Tk5Oj+fPnKzEx0XXUUTpxxDA+Pl433nijJkyYcEr3QV37jZNHHH//uft9R8Grr76qGTNmaPr06brtttuUk5MjSfr22281Y8YMJSYmatasWdq0aVPtL8z/467X5+Q+a/bs2a7PaYsWLfTAAw+oc+fOkk50BJzcn8ybN0+S9PLLL+uyyy7T1KlTtWDBAtdnad68efrmm29cj//by3379tWzzz6rxMRETZw4Ud9++63rdjV9znNycnTDDTdo8uTJuvnmm6t9ZgFvRAAEGqHrrrtOU6dO1YgRIzRhwgRJ0hNPPCFJ+ve//62BAwfqk08+UXJyspo1a6bPP/9c48aN05gxY3T99dfr6quvdj2W3W7Xn/70J82bN09ffvml3nzzTS1ZskRbtmw57XP36dNHAQEB2rZtm6QTX0TGjBkjPz+/M3q8/fv365lnntGXX37pqvm9995T+/btz2oMNm/e7GoBHTFihH766SddccUVp9xu1qxZ+ve//+364rh8+XLNnj1bW7du1U8//aQvvvhCSUlJ6tixo+tL4m/t3LlTsbGxp2yPj49Xx44dJUkVFRUaPny4li9frr/+9a966qmnJNX8WpxUWlqqf/7zn/rXv/6ld955R4cPH9b69euVlJSkzz77TElJSdW+bL3xxhvy9fVVUlKSvvjiC4WHh+vpp58+pbbu3burb9++ri9MTqdTy5Yt09VXX6309HQtWrRIL774or744gstWLBAt912m8rKyvToo4+qU6dOevvtt3X8+HHdc889Wrx4sZYvX65XXnlFDz74oDIzM7V06VJFR0dr+fLl+uijj3To0CEVFRVVq+Hee+/VBRdcoLvvvlvXX3+9Hn30UbVu3Vpffvmlli1bpr179+qdd96RdCLAjx49Wv/5z3904YUXntH4X3TRRerVq1e1bU888YRrjH19fV3bf/rpJ61YsUIfffSRVqxYoZtuukm33367pBNtcDfccIOSkpL0+OOP64cffjjluTZu3Ki33npL77//vr744gtNmTJF8+fPV3BwsMaNG+cKR+np6crLy9PIkSPrfK169OihlStXaty4cac839tvv63+/ftX21ZQUCCn06m2bdu6tkVERCg7O1vZ2dmKjIx0bY+MjFRxcbFKSkqUlZVV7bMVGRmp7OxsZWVlKTw8XBaL5ZTHO919TgaPunz77beyWCzq3r37Gd3+TD+HN9xwg3JycjR06FDNmzdPzz77rCorK9WzZ0/XbTIyMvSvf/1LTz31lJ566ildfPHFSkpK0siRI/Xhhx9KOhGuFyxYoE8//VT//ve/tXr1au3cubPG+u666y7NnDnT9ZncsGGDvv76a0lSdna2brvtNv3nP/+pFtalM9tvSJKvr2+1z91vrVixQvv27dOnn36qzz//XPHx8br//vslSYsXL9aiRYuUlJSk//mf/9GPP/5Y11BLct/rs3nzZo0YMeKU7eHh4a6wJ51o7//ggw/0wQcfaNmyZVq/fr0+++wzffnll+rRo4cWLlxYZ00Oh0NBQUFKSkrSc889p3vvvVdHjx6t9XP+8MMPa8CAAfr3v/+t+++/XwcOHDijvx9oqvzMLgDA2XvvvffUtm1bpaWl6ZZbbtGQIUMUGhoq6UQ43Lx5s/7xj3/o4MGD2r9/vwYMGFDjYx08eFAVFRUaP368pBNfAsePH6/169fXOLdt+vTp+uKLLzRgwACtWLFC99xzj+vLfG2PN2TIELVv315RUVHnPQaDBw/W66+/LulEwHn11Vd100036euvv3b9Ai1JHTt2VK9evbR69WoNGzZMP/zwgx577DE5HA75+vpq1qxZriD9+y/dkuTj4yOn01lrLf7+/q4g3rt3b9dRvrpei4SEBEknxig0NFTHjx9XSkqKJk6cqJCQEEknjuadDCVr165VUVGRNmzYIOlEcDr5uv/e3Llz9eGHHyoxMVHr1q1T+/bt1bt3b3300UcaOnSo60vosGHD1LZtW+3cubPauG3dulW5ubmaP39+tbHYu3evRo4cqVtuuUVZWVm65JJL9L//+79q2bJlrWO0bt06ffzxx/Lx8VFAQIDmzJmj9957T7fccoukE6/n6fj4+Jz3PK+1a9fq0KFD1eZWFRYW6tixY5o0aZIefvhhrV69Wpdccon+v//v/zvl/uvXr9dll13mCl+JiYl67LHHZLPZNGvWLD300EP6wx/+oGXLlmnmzJmyWCx1vlY1/b01cTqd1V4fSTIMQ76+vqe9TpIsFosMw6h2nWEYslgstT7e78f75H1O59dff9X06dMlnfjxJzIyUq+88oqCgoLO6O/q2bPnGX0OIyMjlZSUpJ9//lk//vijfvzxR1155ZVauHCh6wetk2H65Hv75NG3Tp066aeffpIkPfnkk1q3bp1ee+01/fLLL6qoqFBpaalat259ynOWlpZq06ZNOn78uJ5//nnXtj179qh///7y8/PTwIEDT/t3ncl+oy5r1qzRjh07NHPmTEkn3gNlZWWSpMmTJ+v2229XfHy8hg8frptvvvm0j+Gp1+f377O33nrL1SmSl5enf//735JOHK0PDg6WdGKfkJiYqObNm0uSrr32Wr322muuH+tqc80110g6sb/t2bOnNm3apG3bttX4Od+wYYP++te/SpI6d+6sIUOGnNHfDzRVBECgEevXr5/uueceLVy4UH369FF0dLSeeuopbd++XTNnztSQIUNkt9tr/QLtcDhO+0XwZHve6UydOlUzZ87U9ddfr+Li4mq/wtf1eCf/s69PFotF8+bN0wsvvKD8/HxXa+BJs2fP1ooVK5Sfn6+xY8eqRYsWkqTPP/9cqamp+uGHH/SXv/xFf/jDH6odHZWkgQMHauvWrRo9enS17S+99JI6deqk2NhY+fv7u7b/9m+v67UIDAysdj/DMOTn51ftNr89kuV0OnXvvfcqPj5e0okFXyoqKk47JuPGjdPjjz+ugwcPaunSpa6/q6Yv/na7vdrf4XA4FBMTo08//dS1LScnR23btpW/v7+Sk5O1ceNG/fDDD5o1a5befPNNXXDBBaet5XTP63Q6q73HanpfnBz/k1/4TvrXv/6lsrIy3XDDDTU+52+fa/r06brrrrtcl48cOaJWrVppzpw5Gj16tL7//nutX79eL730kr755ptqr83pvsifHLPBgwfLbrdr+/bt+uqrr/TJJ5+47lPba3W2n4PQ0FAZhqFjx465wsqRI0cUERGh4OBg1xF56cTr1KpVKzVv3lzt27ev1p545MgRRUZGqkOHDsrNza32xf3kdTXd53Q6depU7ah2bX77vj75JT8kJOSMPoeLFy/WrFmz1L17d3Xv3l1XX321Pv/8c7355puu2wYEBFS7z2/fzyddc8016tWrl0aOHKlJkyZp27ZtNe4fnU6nDMPQv/71L1dgOnr0qAIDA1VQUKCAgABX58Pvncl+oy5Op1M33XST5s6dK+nEmB0/flySdMcdd2jmzJn6/vvvlZSUpHfeeUefffbZKY/hqddn0KBB+umnn1x/70033aSbbrpJ0onQd/Iz9Nv3fV37hN/W8/uFZH6/Xzz5Q0hNn/Pf/5BU0+sGeAtaQIFGbsqUKerfv7+rnfK7777Tddddp8svv1yhoaHasGGDHA6HpBP/af4+2HXr1k1+fn6ueRQ5OTn6z3/+o0suuaTG54yIiFCvXr107733un5dPtfHO11N52Lt2rWKioqq1iJ30rhx45SWlqalS5e65lCtWbNG119/vQYNGqQ///nPuvzyy0/bCvaHP/xBn376qb777jvXtnXr1umDDz5Q7969a62ptteiJvHx8fr2229dLZW//VI3YsQIffTRR6qsrJTT6dQDDzygJUuWnPZx/Pz8NHv2bL3//vvatWuX64jssGHD9N1337lWct24caOysrI0YMAA+fr6ur5oDRw4UIcOHXLNLdq9e7cmTJignJwcPf3003rllVc0duxY3Xffferevbv2799f6981YsQIffjhhzIMQ5WVlVq6dGmt77GTrrzySlcL2skvcDt37tQLL7xQ7YeHk3+zw+E45Qv9iBEj9O9//9sVaj7++GNdd911kqQ5c+Zo9+7dSkxM1COPPKLCwsJT5geNHDlSX3/9tWs+4bJly9S6dWvX3KZZs2bpkUceUa9evVytk2fzWp0JPz8/jRo1SkuXLpUk7dmzR+np6RoyZIhGjBihbdu26eDBg5JOhOOTR5cTEhK0bNky2e12FRYW6t///rfGjh2ryMhIderUydXOuH79elksFvXs2VNjxozR6tWrlZ+fL8Mw9Mknn2js2LHnXLsk11Fm6cRc4JNjfKafw6NHj+r55593HQEzDEP79+9X3759z7iGwsJC7dixQ3feeafGjx+v7Oxs/frrrzUeqQsODtbAgQP1j3/8w3X/q666SsnJyXU+19nsN377ufutESNG6LPPPnPNHX3++ed19913y263a8yYMSorK9NVV12lRYsWae/evWd05Kwm5/v6/OlPf9LKlSu1YsUK1z7Obre73l+nO4I8cuRILVu2zDVX+YMPPtBFF12kgICAavX8/PPPp7SdnpxXnpaWpgMHDuiiiy6q9XM+cuRI148zmZmZZ9wyCzRV/AQCNAEPPPCApk2bpvXr12v+/PlavHixnn/+efn7+ys2Nla//vqrJOnSSy/Vk08+We2+/v7+euWVV/Too4/qxRdflMPh0Pz58zV06NBan3P69Om699579eKLL57x453uP92JEydq3rx5evHFF0/5Ql+bk3MAfXx8ZLfb1bp1a7388sun/aIREBCgyy67TBs2bHC1L1166aVat26dpkyZoubNm6tVq1bVFos5qXPnznrttdf03HPP6e9//7trHtarr76qnj17ymaz1Vhjba9FTYYNG6bZs2fryiuvVLNmzdSjRw/X0YfbbrtNf//73zVjxgw5HA716dOn1jkzs2fPVkJCgm655RbX0ZDu3btr0aJFuv322+VwONSsWTO99tpratmypbp3767AwEBdccUV+vTTT/XCCy9o8eLFqqiokGEYWrx4saKjo3Xddddp4cKFmjJligICAtSrVy/XYiI1uf/++/Xoo49q6tSpqqqq0siRI/XHP/6x1vtIUuvWrfXBBx/oqaee0uuvvy6LxaKgoCA99thjGj58eLXbhoWFqX///po8ebLrdCfSiS/SN998s2688Ub5+PgoODhYL730knx8fHTnnXfq8ccf13PPPScfHx/dfvvtio6Orva4w4cP1/XXX6/rrrvO9fqfrEWSLr/8ci1ZsqRawDvb1+pMLFq0SPfff7+mTJkiHx8fLV682NV6+8QTT2jBggWqqqpSp06d9Pe//13SiQVhTrYBVlVV6corr9TFF18s6cQCSQ888IBeffVVBQQE6Pnnn5fFYlHv3r01f/58XXfddaqqqtKAAQNqbDE8U3feeacefPBBffLJJ+rXr5/69esn6cw/h4sWLdKzzz6radOmKSAgQHa7XUOHDtXf/va3M64hJCREt9xyi2bMmKHmzZsrIiJCsbGxOnToULV5eb/19NNP65FHHtHUqVNVWVnpWgiots+9dHb7jd9+7p599lnX9lmzZiknJ8e1sEr79u315JNPys/PT/fee6/uvPNO+fn5ycfHR48//vgpR0DPxvm+PpGRkfrkk0/00ksvueYylpSUaODAgVq6dOlpW2yvuOIKZWVladasWXI6nercubNrnuyf/vQnLVy4UCkpKerWrdspLdOpqalaunSpnE6nnn32WbVq1arWz/miRYt0zz33aNKkSYqMjKzzxzugqfMxzndyBQCg3uzYsUNbtmxxnc/uH//4h7Zt23balTYBwNv06tVLGzduPG23B4AzwxFAAGhAunbtqjfffFNLly51/ep/ul/cAQAAzgVHAAEAAADAS7AIDAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4iSa5CmhBQYmcTta28bTQ0GDl5xebXYZXYuzNxfibh7E3D2NvHsbePIy9eRj7M2ex+KhNmxY1Xt8kA6DTaRAATcK4m4exNxfjbx7G3jyMvXkYe/Mw9uZh7OsHLaAAAAAA4CUIgAAAAADgJQiAAAAAAOAlCIAAAAAA4CXcGgBfeuklTZ48WZMnT9bixYslSRs2bNDUqVM1fvx4Pfvss6e9X2Zmpq6++mpNnDhRf/rTn1RSUuLOMgEAAADAK7gtAG7YsEHfffedli9frhUrVigtLU1fffWV7r33Xr3yyiv6+uuvtXPnTqWkpJxy34ceekhz587VN998owsuuECvvPKKu8oEAAAAAK/htgAYFhamhQsXKiAgQP7+/oqJidHBgwfVuXNndezYUX5+fpo6daq++eabaverqqrSpk2bNGHCBElSYmLiKbcBAAAAAJw9t50HsEePHq5/Hzx4UCtXrtQ111yjsLAw1/bw8HDl5ORUu19BQYGCg4Pl53eitLCwsFNuU5fQ0ODzqBznIyyspdkleC3G3lyMv3kYe/Mw9uZh7M3D2JuHsa8fbj8R/P79+3Xrrbfq7rvvlq+vrw4ePOi6zjAM+fj4VLv96bb9/nJd8vOLOVGkCcLCWio3t8jsMrwSY28uxt88jL15GHvzMPbmYezNw9ifOYvFp9YDYm5dBMZqter666/X//7v/2rGjBmKjIxUbm6u6/rc3FyFh4dXu0/btm1VVFQkh8NR420AAAAAAGfPbQEwKytL8+fP19NPP63JkydLkgYMGKADBw7o0KFDcjgc+uqrr3TppZdWu5+/v78GDx6sr7/+WpK0YsWKU24DAAAAADh7bmsBffvtt1VRUaEnn3zStW3OnDl68skn9ec//1kVFRWKj4/XxIkTJUn33XefxowZo4SEBC1atEgLFy7Uq6++qvbt22vJkiXuKhMAAAAAvIaPYRhNbrIccwDNQW+2eRh7czH+5mHszcPYm4exNw9jbx7G/syZOgcQAAAAANBwEAABAAAAwEsQAAEAAADASxAAAQAAAMBLEAABAAAAwEu47TQQAIDGY2NatpJS0pVfWKHQkEAlxsdoWL9Is8sCAAD1jAAIAF5uY1q23lu5R5V2pyQpv7BC763cI0mEQAAAmhhaQAHAyyWlpLvC30mVdqeSUtJNqggAALgLARAAvFx+YcVZbQcAAI0XARAAvFxoSOBZbQcAAI0XARAAvFxifIwC/Kr/dxDgZ1FifIxJFQEAAHdhERgA8HInF3phFVAAAJo+AiAAQMP6RRL4AADwArSAAgAAAICXIAACAAAAgJcgAAIAAACAlyAAAgAAAICXIAACAAAAgJcgAAIAAACAlyAAAgAAAICXIAACAAAAgJcgAAIAAACAlyAAAgAAAICXIAACAAAAgJcgAAIAAACAlyAAAgAAAICXIAACAAAAgJcgAAIAAACAlyAAAgAAAICXIAACAAAAgJfwM7sAAADgORvTspWUkq78wgqFhgQqMT5Gw/pFml0WAMBDCIAAAHiJjWnZem/lHlXanZKk/MIKvbdyjyQRAgHAS9ACCgCAl0hKSXeFv5Mq7U4lpaSbVBEAwNMIgAAAeIn8woqz2g4AaHoIgAAAeInQkMCz2g4AaHoIgAAAeInE+BgF+FX/rz/Az6LE+BiTKgIAeBqLwAAAmhRWuazZyXFgfADAexEAAQBNBqtc1m1Yv0jGAgC8GC2gAIAmg1UuAQCoHQEQANBksMolAAC1IwACAJoMVrkEAKB2BEAAQJPBKpcAANSORWAAAE0Gq1wCAFA7AiAAoElhlUsAAGpGCygAAAAAeAmOAAIA0IBwInsAgDsRAAEAaCA4kT0AwN1oAQUAoIHgRPYAAHcjAAIA0EBwInsAgLsRAAEAaCA4kT0AwN0IgAAANBCcyB4A4G4sAgMAQAPBiewBAO5GAAQAoAHhRPYAAHeiBRQAAAAAvAQBEAAAAAC8BAEQAAAAALwEARAAAAAAvAQBEAAAAAC8BAEQAAAAALwEARAAAAAAvAQBEAAAAAC8BAEQAAAAALyEnzsfvLi4WHPmzNFrr72m9PR0LVmyxHVdTk6OBgwYoNdff73afZYvX65nnnlGoaGhkqRRo0bpjjvucGeZAAAAAOAV3BYAt23bpvvvv18HDx6UJMXHxys+Pl6SlJubq6uuukr33HPPKffbuXOnFi5cqClTprirNAAAAADwSm5rAV26dKkWLVqk8PDwU65bvHix5syZoy5dupxy3Y4dO7R8+XJNnTpVd955p44fP+6uEgEAAADAq/gYhmG48wnGjBmj999/X9HR0ZKkgwcP6rrrrtN///tfBQQEnHL7+fPn68Ybb1RsbKyWLFmizMxMPfPMM+4sEQAAAAC8glvnAJ7OJ598orlz5542/EnSyy+/7Pr3TTfdpHHjxp31c+TnF8vpdGuuxWmEhbVUbm6R2WV4JcbeXIy/eRh78zD25mHszcPYm4exP3MWi49CQ4Nrvt6DtUiSkpOTddlll532uqKiIr377ruuy4ZhyNfX10OVAQAAAEDT5tEAePToUZWXl6tjx46nvb558+Z66623tG3bNknShx9+eE5HAAEAAAAAp/JoC6jNZlNkZOQp2++77z6NGTNGCQkJeu655/Tggw+qvLxcXbp00eLFiz1ZIgAAAAA0WW5fBMYMzAE0B73Z5mHszcX4m4exNw9jbx7G3jyMvXkY+zPX4OYAAgAAAADMQQAEAAAAAC9BAAQAAAAAL0EABAAAAAAvQQAEAAAAAC9BAAQAAAAAL0EABAAAAAAvQQAEAAAAAC9BAAQAAAAAL0EABAAAAAAvQQAEAAAAAC9BAAQAAAAAL+FndgEAAABm2ZiWraSUdOUXVig0JFCJ8TEa1i/S7LIAwG0IgAAAwCttTMvWeyv3qNLulCTlF1bovZV7JIkQCKDJogUUAAB4paSUdFf4O6nS7lRSSrpJFQGA+xEAAQCAV8ovrDir7QDQFBAAAQCAVwoNCTyr7QDQFDAHEAAAeKXE+JhqcwAlKcDPosT4GBOrApomFlxqOAiAAADAK5388smXUsC9WHCpYSEAAgAArzWsXyRfQAE3q23BJT5/nsccQAAAAABuw4JLDQsBEAAAAIDbsOBSw0IABAAAAOA2ifExCvCrHjtYcMk8zAEEAAAA4DYsuNSwEAABAAAAuBULLjUctIACAAAAgJcgAAIAAACAlyAAAgAAAICXIAACAAAAgJcgAAIAAACAlyAAAgAAAICXIAACAAAAwFkqq7CrsKTS7DLOGucBBAAA9WJjWjYnegbQ5GXmlSg51aYNO7MV3jpID914sdklnRUCIAAAOG8b07L13so9qrQ7JUn5hRV6b+UeSSIEAmj0nE5D29PzlWw9rLSDBfLz9dHFfSI0cUgns0s7awRAAABw3pJS0l3h76RKu1NJKekEQACNVkl5ldZvy9LqVJvyjperTctAzbi0m+IHdFBIiwCzyzsnBEAAAHDe8gsrzmo7ADRkttxirbbatCEtW5VVTvWMbqVZo7trUI928vNt3MuoEAABAMB5Cw0JPG3YCw0JNKEaADh7DqdTW/efaPPc8+sx+ftZNKRvhMbGRatTREuzy6s3BEAAAHDeEuNjqs0BlKQAP4sS42NMrAoA6lZcVqV12zK1JtXmWsTqilExunRABwUH+ZtdXr0jAAIAgPN2cp4fq4ACaCx+zSlSstWmH3blqMruVO9OrTUnoacG9giVr6Vxt3nWhgAIAADqxbB+kQQ+AA2aw+lU6r48JW8+rH224wrws+iSCyKVEBut6PBgs8vzCAIgAAAAgCatsLRS67Zmas2WDBUUVahdq2aaPbq7Rg5orxbNml6bZ20IgAAAAACapIPZhUrebNOPu4/I7nCqb5c2umZ8Tw2IaSeLxcfs8kxBAAQAAADQZNgdTln35mqV9bDSMwoV6O+rkQPaKyE2Wh3atTC7PNMRAAEAAAA0eseLK5SyNVNrtmboeHGlwtsE6aqEHhp+YXs1b0bsOYmRAAAAANBopWceV7LVpk27j8jhNHRBt7a6YVK0LugWKouPd7Z51oYACAAAAKBRqbI7tXnPEa2y2nQgq1DNAnw1elCUxsRFK7Jtc7PLa9AIgAAAAAAahYKiCq3dkqGUrRkqLK1SZNvmunpcT11yQaSCAok2Z4JRAgAAANBgGYah3QeO6rPkvbLuzZXTaah/TKgSBkerb5e2tHmeJQIgAAAAgAanyu7Qj7uOKNlq06GcIgUF+ikhLlpjYqMU3oY2z3NFAAQAAADQYBwtLNeaLRlK2Zqp4rIqdWjXQrfN7K8LOrdWswDiy/liBAEAAACYyjAM7Tt8TKusNm3ZlydDhgZ2b6excdHq3bmNwsNDlJtbZHaZTQIBEAAAAIApKqoc+nFXjlZttsmWW6wWzfw0/uKOGjMoSu1aB5ldXpNEAAQAAADgUXnHy7QmNUPrtmWqpNyu6LBgXT+pt4b0jVCgv6/Z5TVpBEAAAAAAbmcYhvYcKtAqq01bf86Tj3wU27OdEuKi1bNja/mwmqdHEAABAAAAuE1FpUMb0rK12mpTRl6JgoP8ddnQzho9KEptQ5qZXZ7XIQACAAAAqHdHjpVptdWm77ZnqbTCrk4Rwbrxsj4a0jdc/n60eZqFAAgAAACgXhiGobSDR5W82abt6fmyWHwU1ytMCXHR6h7VijbPBoAACAAAAOC8lFXYtWFntpKtNmUfLVVIc39NuaSLRg2KUpuWgWaXh98gAAIAAAA4JzlHS5Vstem7HVkqr3Soa/uWunlKXw3uHS5/P4vZ5eE0CIAAAAAAzpjTMLTzl3ytstq085ej8rX46KI+4UqIi1ZMh1Zml4c6EAABAAAA1Km03K7vd2QpOdWmIwVlahUcoMtHdFX8wA5qFUybZ2Ph1gBYXFysOXPm6LXXXlN0dLTuueceWa1WBQUFSZJuv/12jRs3rtp9MjMzdddddyk/P19du3bV008/rRYtWrizTAAAAAA1yMwrUXKqTRt2ZKuiyqGYqBBdPrKrBvcKl58vbZ6NjdsC4LZt23T//ffr4MGDrm07d+7Uhx9+qPDw8Brv99BDD2nu3LmaPHmyXn75Zb3yyiu666673FUmAABws41p2UpKSVd+YYVCQwKVGB+jYf0izS4LQC2cTkPb0/OVbD2stIMF8vP10ZA+EUoYHK0ukSFml4fz4LbIvnTpUi1atMgV9srKypSZmal7771XU6dO1QsvvCCn01ntPlVVVdq0aZMmTJggSUpMTNQ333zjrhIBAICbbUzL1nsr9yi/sEKSlF9YofdW7tHGtGyTKwNwOiXlVfrmx1+18PWNemHZdmXmlyrx0m56ev5w/WFKX8JfE+C2I4CPPfZYtct5eXkaOnSoFi1apJYtW+rWW2/VZ599ptmzZ7tuU1BQoODgYPn5nSgrLCxMOTk57ioRAAC4WVJKuirt1X/wrbQ7lZSSzlFAoAGx5RYr2WrTxrRsVVY51TO6lWaN7q5BPdrR5tnEeGwRmI4dO+rll192XZ43b55WrFhRLQAahnHKySHP5WSRoaHB514ozktYWEuzS/BajL25GH/zMPbmOZOxP/r/jvydbjuv3blj7MzTlMbe4XDqp13Z+uq7A9r+c54C/CyKj43WlBHd1C2q4a3m2ZTG3kweC4B79+7VwYMHXe2dhmG4jvSd1LZtWxUVFcnhcMjX11e5ubm1zhesSX5+sZxOo17qxpkLC2up3Nwis8vwSoy9uRh/8zD25jnTsW8bEuhq//z9dl67c8P73jxNZeyLy6q0blum1qTaXHNzrxgVo0sHdFBwkL8kNbi/s6mMvSdYLD61HhDzWAA0DEOPP/64hg4dqubNm+uTTz7RjBkzqt3G399fgwcP1tdff62pU6dqxYoVuvTSSz1VIgAAqGeJ8TF6b+Weam2gAX4WJcbHmFgV4J1+zSnSKqtNP+7KUZXdqd6dWmtOQk8N7BEqXwttnt7CYwGwd+/euuWWW3TVVVfJbrdr/PjxmjJliiTpvvvu05gxY5SQkKBFixZp4cKFevXVV9W+fXstWbLEUyUCAIB6dnKeH6uAAuawO5zasj9PyZsPa5/tuAL8LLrkgkglxEYrOpxpU97IxzCMJtcrSQuoOTg0bx7G3lyMv3kYe/Mw9uZh7M3TmMa+sLRSKVsztXZLhgqKKtSuVTONiY3WyAHt1aKZv9nlnbXGNPZmazAtoAAAAADc62B2oZI32/Tj7hzZHYb6dWmjeeN7qX9MqCyWs19cEU0PARAAAABoxOwOpzbvPaJkq03pGYUK9PfVyAEdlBAbrQ7tWphdHhoYAiAAAADQCB0vrtDarZlauzVDx4srFd4mSFcl9NDwC9ureTO+5uP0eGcAAAAAjUh65nElW23atPuIHE5DF3RrqxsmddQF3drKcg7n0IZ3IQACAAAADVyV3alNe3KUbLXpQFaRmgX4avSgKI2Ji1Zk2+Zml4dGhAAIAAAANFAFRRVauyVDKVszVFhapfahzXX1uJ665IJIBQXyVR5nj3cNAAAA0IAYhqGfM060eVr35srpNNQ/JlRjB3dU3y5t5EObJ84DARAAAABoAKrsDv2464hWWQ/r15xiBQX6KSEuWmNioxTehjZP1A8CIAAAAGCio4XlWrMlQylbM1VcVqUO7Vpo3oReGtYvQs0C+LqO+sU7CgAAAPAwwzC07/AxrbLatGVfngwZGti9ncbGRat3Z9o84T4EQAAAAMBDKqoc+nFXjlZttsmWW6wWzfw04eKOGj0oSu1aB5ldHrwAARAAAABws7xjZVq9JUPrt2WqpNyu6LBgXT+pt4b0jVCgv6/Z5cGLEAABAAAANzAMQ7sPFSjZatPWn/PkIx/F9mynhLho9ezYmjZPmIIACAAAANSjikqHNqRla7XVpoy8EgUH+euyoZ01elCU2oY0M7s8eDkCIAAAAFAPjhSUanVqhtZvz1JZhV2dI1rqxsv6aEjfcPn70eaJhoEACAAAAJwjp2Fo18GjSt5s0/b0fFksPorrFaaxcR0VExVCmycaHAIgAAAAcJbKKuzasDNbyVabso+WKqS5v6Zc0kWjBkWpTctAs8sDakQABAAAXmFjWraSUtKVX1ih0JBAJcbHaFi/SLPLQiOTfbRUyVabvt+RpfJKh7q2D9HNU/pqcO9w+ftZzC4PqBMBEAAANHkb07L13so9qrQ7JUn5hRV6b+UeSSIEok5Ow9DOX/K1bsVOpe45Il+Ljy7qE66EuGjFdGhldnnAWSEAAgCAJi8pJd0V/k6qtDuVlJJOAESNSsvt+m5Hllan2nSkoExtQwJ1+Yiuih/YQa2CafNE40QABAAATV5+YcVZbYd3y8wrUXKqTRt2ZKuiyqHuUa00Y2Q3TRjeTccKSswuDzgvBEAAcCPmHAFnzx2fm9CQwNOGvdAQjuLgBKfT0Lb0PCVbbdp1sEB+vj4a0idCCYOj1SUyRJKY44cmgQAIAG7CnCPg7Lnrc5MYH1PtcSUpwM+ixPiY8ysYjV5JeZXWbzvR5pl3vFxtWgYq8dJuunRgB4U0DzC7PKDeEQABwE2YcwScPXd9bk7elyPyOMmWW6xkq00b07JVWeVUz+hWmj26uwb1bCdfC0f60HQRAAHATZhzBJw9d35uhvWLJPB5OYfTqa37T7R57vn1mPz9LBraN0IJcdHqFNHS7PIAjyAAAoCbMOcIOHt8buAOxWVVWrctU2tSba4jwLNGxWjkgA4KDvI3uzzAowiAAOAmzDkCzh6fG9SnX3OKtMpq04+7clRld6p3p9aak9BTA3uE0uYJr0UABAA3Yc4RcPb43OB82R1Obdmfp1WbD2u/7bgC/C0afkGkxsRFKzos2OzyANMRAAHAjZhzBJw9Pjc4F4WllUrZmqm1WzJUUFShdq2aafbo7ho5oL1aNKPNEziJAAgAAIBG60BWoZKtNv20O0d2h6F+Xdpo3vhe6h8TKovFx+zygAaHAAgAAIBGxe5wavPeI0q22pSeUahAf19dOqCDEuKi1T60hdnlAQ0aARAAAACNwvHiCq39f22ex0sqFd4mSFcl9NDwC9ureTO+1gJngk8KAAAAGrT0zONK3mzTpj1H5HAaurBbqBLionVBt7ay+NDmCZwNAiAAAAAanCq7U5v25CjZatOBrCI1C/DV6EFRGhMXrci2zc0uD2i0CIAAAABoMAqKKrRmS4bWbc1QYWmV2oc219XjeuqSCyIVFMhXV+B88SkCAACAqQzD0M8Zx5Vstcm6N1dOp6EB3dspIS5afbu0kQ9tnkC9IQACAADAFFV2h37YdaLN89ecYgUF+ikhLlpj4qIV3jrI7PKAJokACAAAAI86WliuNVsylLI1U8VlVYpq10LXTuilYf0iFRjga3Z5QJNGAAQAAIDbGYahfYePaZXVpi378mTI0MDu7TQ2Llq9O9PmCXgKARAAAABuU1Hl0A9p2Uq22mTLLVGLZn6acHFHjR4UpXa0eQIeRwAEAABAvcs7VqbVWzK0flumSsrtig4L1vWTemtI3wgF+tPmCZiFAAgAAIB6YRiGdh8qULLVpq0/58lHPorteWI1z54dW9PmCTQABEAAAACcl/JKuzamnVjNMzOvRMFB/rpsaGeNHhSltiHNzC4PwG/UGQDT09OVmpqqK664QnfccYd27typRx99VEOHDvVEfQAAAGigjhSUanVqhtZvz1JZhV2dI1rqD5P76OI+4fL3o80TaIjqDICLFi3S7NmztXbtWuXk5Oixxx7TkiVL9Mknn3iiPgAAADQgTsPQroNHtWqzTTvS82Wx+CiuV5jGxnVUTFQIbZ5AA1dnAKyoqNC0adP0yCOPaNKkSRoyZIiqqqo8URsAAAAaiLIKuzbsPLGaZ/bRUoU099fU4V0UPzBKbVoGml0egDNUZwCsrKxUXl6e1q5dq9dff115eXmqqKjwRG0AALjFxrRsJaWkK7+wQqEhgUqMj9GwfpFmlwU0SNlHS5Vsten7HVkqr3Soa/sQ3Tylrwb3Dpe/n8Xs8gCcpToD4JVXXqnRo0dr0qRJ6t69u0aNGqXbbrvNE7UBAJqYhhC8NqZl672Ve1Rpd0qS8gsr9N7KPZJECAT+H6dhaOcv+VpltWnnL0fla/HRxX3ClRDXUd06hJhdHoDzUGcAnDt3rubMmSOL5cQvPMuXL1ebNm3cXhgAoGlpKMErKSXdVcNJlXanklLSCYDweqXldn23I0urU206UlCmVsEBunxEV8UP7KBWwbR5Ak1BnQGwpKREzzzzjNLT0/X888/r2Wef1V//+le1aNHCE/UBAJqIhhK88gtPP42hpu2AN8jIK9Fqq00bdmarosqh7lGtNGNkN8X1CpOfL22eQFNSZwB89NFHFR4ervz8fAUGBqq4uFh/+9vf9Mwzz3iiPgBAE9FQgldoSOBpnzM0hKMb8C5Op6Ft6XlKttq062CB/HwtGtI3XGPjOqpzZEuzywPgJnUGwN27d+uJJ55QSkqKgoKC9PTTT2vKlCmeqA0A0IQ0lOCVGB9TrRVVkgL8LEqMj/FoHYBZSsqrtH7biTbPvOPlatMyUImXdtOlAzsopHmA2eUBcLM6A+DJuX8nORyOU7YBAFCXhhK8Trabmr0YDeBptiPFSk61aePObFXanerZsbVmj+6uQT3byZfvdoDXqDMAXnTRRXrqqadUXl6u9evX66OPPtLFF1/sidoAAE1IQwpew/pFEvjgFRxOp7buP9HmuefXY/L3s2ho3wglxEWrUwRtnoA3qjMA3nnnnXrjjTfUsmVLPfvssxo5cqTmz5/vidoAAE0MwQvwjOKyKq3blqk1qTbXDy6zRsVo5IAOCg7yN7s8ACaqMwCmpKRo/vz51ULfihUrdPnll7uzLgAAAJylX3OKtMpq04+7clRld6p3p9a6amxPDezeThaLj9nlAWgAagyAq1evlt1u1+LFi2UYhgzDkCTZ7Xa9+OKLBEAAAIAGwO5wKnVfrpKtNu23HVeAv0XDL4jUmLhoRYcFm10egAamxgC4e/du/fDDD8rPz9f777//f3fw89P111/vidoAAABQg8KSSqVsy9TaLRkqKKpQWOtmunJMd43o314tmtHmCeD0agyAJ9s+P/roI1199dWerAkAAAA12H+4QJ+t2qefdufI7jDUr0sbzRvfS/1jQmnzBFCnOucAzpkzR2+++abWrVsnu92u4cOH649//KP8/Oq8KwAAAOqB3eHU5j1HlGy1KT2zUIEBvrp0QAclxEWrfWgLs8sD0IjUmeKeffZZ7d69W9ddd52cTqc++eQTLV68WPfee68n6gMAAPBax4srtHbriTbP4yWVCm8TpJsvv0ADurRV82b8GA/g7NW551i3bp2WLVsmf/8TveSjRo3StGnTCIAAAABuYBiGfsksVLLVpk17jsjhNHRht1AlxEXrgm5tFREeotzcIrPLBNBI1RkADcNwhT9JCggIqHa5NsXFxZozZ45ee+01RUdH65NPPtEHH3wgHx8fXXDBBXrooYcUEBBQ7T7Lly/XM888o9DQUEknAucdd9xxNn8TAABAo1Nld2rTnhyt2mzTwewiBQX6anRslBJioxXRtrnZ5QFoIuoMgL1799bjjz+ua665Rj4+Pvrwww/Vs2fPOh9427Ztuv/++3Xw4EFJ0oEDB/T2228rKSlJLVq00MKFC/XPf/7zlBVFd+7cqYULF2rKlCnn9AcBAAA0JgVFFVqzJUPrtmaosLRK7UOb6+pxPXXJBZEKCqTNE0D9qnGvUlVVJX9/fy1atEiPPPKI5syZI8MwNGLECD3wwAN1PvDSpUu1aNEi3X333ZJOHDlctGiRgoNPnI+mZ8+eyszMPOV+O3bs0MGDB/X666+rV69eeuCBB9SqVatz/fsAAAAaHMMw9HPGca3abFPqvlw5nYYGdG+nhLho9e3SRj4+rOYJwD18jJNneP+dSy65RLNnz9ZVV12liIiIc36CMWPG6P3331d0dLRr29GjR3XFFVfoiSee0JAhQ6rdfv78+brxxhsVGxurJUuWKDMzU88888w5Pz8AAEBDUVHl0PotNn353QH9knFcLYL8Ne7iTpo8vKsiWc0TgAfUGADT0tL0ySef6JtvvtGQIUN0zTXXnBLWzsTvA2BOTo5uuukmTZw4UfPnz6/1vsePH9e4ceP0008/ndVz5ucXy+k87Z8FNwoLa8mkdJMw9uZi/M3D2JuHsT87+cfLT7R5bstUcVmVotq1UEJctIb1i1RggO9ZPRZjbx7G3jyM/ZmzWHwUGhpc4/U1toD269dPDz/8sBYuXKivvvpKTz31lMrKyjR37lxdfvnlatHi7H+lSk9P10033aR58+bpxhtvPOX6oqIiLVu2zDUv0DAM+fqe3U4RAACgITAMQ/sOH9Mq64k2T0ka1CNMCXHR6t2pNW2eAExR58zi5s2ba/bs2Zo9e7b27Nmjf/7zn4qPj9fmzZvP6omKi4v1hz/8QX/5y190+eWX1/hcb731lgYNGqQBAwboww8/1Lhx487qeQAAAMxUUeXQD2nZSrbaZMstUYtmfpp4cSeNjo1Su1ZBZpcHwMud8dJSP/74oz799FP98MMPmjZt2lk/0Weffaa8vDz94x//0D/+8Q9JJ9pD/+d//kf33XefxowZo4SEBD333HN68MEHVV5eri5dumjx4sVn/VwAAACelnesTKtTM7R+e6ZKyu3qGB6s6yf11pC+EQr0p6MJQMNQ4xxA6cR8vaSkJC1btkxBQUGaM2fOObd/ehJzAM1Bb7Z5GHtzMf7mYezNw9ifYBiGdh8qULLVpq0/58lHPort2U5jB3dUj+hWbmnzZOzNw9ibh7E/c+c8B/Cmm27Spk2bNGbMGD3++OO6+OKL3VIgAABAY1NeadfGndlKTs1QZl6JgoP8ddnQzho9KEptQ5qZXR4A1KjGADhw4EA98cQTCgsL82Q9AAAADVZOQalWWzP03Y4slVXY1Tmypf4wuY8u7hMufz/aPAE0fDUGwNtvv92TdQAAADRITsPQrgNHtcpq0470fFksPorrFaaxgzsqpkMIq3kCaFTOeBEYAAAAb1JWYdf3O7KUnJqhnKOlCmkRoKnDuyh+YJTatAw0uzwAOCcEQAAAgN/IPlqqZKtN3+/IUnmlQ13bh+jmKX01uHe4/P0sZpcHAOfljAJgZWWlysrK9NsFQ1u3bu2umgAAADzKaRjakZ6vZKtNOw8cla/FRxf3CVdCXEd16xBidnkAUG/qDIAff/yxnnjiCVVVVUk6sdyxj4+Pdu/e7fbiAAAA3Km03K7vdmRpdapNRwrK1Co4QJeP7Kr4gVFq1SLA7PIAoN7VGQDffvttffzxx+rXr58n6gEAAHC7jLwSrbbatGFntiqqHOoe1UozRnZTXK8w+fnS5gmg6aozALZr147wBwAAGj2n09C2n/O0ymrT7kMF8vO1aEjfcI2N66jOkS3NLg8APKLOADhixAj985//VEJCggID/2/FK+YAAgCAxqCkvErrt51o88w7Xq42LQM1M76bRg7ooJDmtHkC8C51BsA33nhDlZWVevjhh13bmAMIAMAJG9OylZSSrvzCCoWGBCoxPkbD+kWaXRYk2Y4Ua5XVph/SslVpd6pnx9aaPbq7BvVsJ18LbZ4AvFOdAXD79u2eqAMAgEZnY1q23lu5R5V2pyQpv7BC763cI0mEQJM4nE5t3Z+nVZtt2nv4mPz9LBrWL0JjYqPVKYI2TwCoMQB+/vnnmj59uv7xj3+c9vobbrjBbUUBAM4MR5/MlZSS7gp/J1XanUpKSed18LCi0kqt25apNVsydLSwQqEhzTRrVIxGDuig4CB/s8sDgAajxgB46NAhSdK+ffs8VgwA4Mxx9Ml8+YUVZ7Ud9e9QdpGSrTb9uDtHVXan+nRuo7lje2pg93ayWHzMLg8AGpwaA+CCBQskSU888YTHigEAnDmOPpkvNCTwtGEvNCTwNLdGfbE7nErdl6tkq037bccV4G/R8AsiNSYuWtFhwWaXBwANWp1zAAEADRNHn8yXGB9T7SisJAX4WZQYH2NiVU1XYUmlUrZmaO3WTBUUVSisdTNdOaa7RvRvrxbNaPMEgDNBAASARoqjT+Y7eaSVeZjudSCrUMlWm37anSO7w1C/rm01b0Iv9e8WSpsnAJwlAiAANFIcfWoYhvWLJPC5gd3h1OY9R5RstSk9s1CBAb66dEAHJcRFq31oC7PLA4BGq84A6HQ69c4772j//v164IEH9NFHH+mmm26Sr6+vJ+oDANSAo09oio4VV2jtlgylbM3U8ZJKRbQJ0lVje2j4Be3VvBm/WwPA+apzT7p48WIdPXpUO3bskCStX79eubm5uv/++91eHACgdhx9QlNgGIZ+yTzR5rlpzxE5nIYu7BaqsYOj1a9rW1l8aPMEgPpSZwDcuHGjli9frsTERAUHB+udd97R9OnTPVEbAABowqrsTv20O0fJVpsOZhcpKNBXo2OjlBAbrYi2zc0uDwCapDoDoJ+fnywWi+tyQECA/PxowQAAAOemoKhCa7ZkKGVrhopKq9Q+tLmuGd9Tw/pFKiiQ7xgA4E517mV79uypjz76SA6HQ7/88oveffdd9e7d2xO1AQCAJsIwDO23HVey1abUfblyOg0N6N5OCXHR6tuljXxo8wQAj6gzAN533316/PHHlZ+fr6uuukojRoxg/h8AADgjlVUO/bjrRJvnr0eK1TzQT2MHR2t0bLTCWweZXR4AeJ06A2BwcLAef/xxT9QCAACaiPzj5VqzJUPrtmWquKxKUe1a6NoJvTSsX6QCA1hJHADMUmcAnDdvXrW2DB8fHwUFBalHjx669dZbFRwc7NYCAQBA42AYhvb+euxEm+f+XEnSoB5hSoiLVu9OrWnzBIAGoM4A2L17d/3666+aM2eOLBaLli9froCAAJWXl+vBBx/U008/7Yk6AQBAA1VR5dDGtGytttpkyy1Ri2Z+mjikk0YPilK7VrR5NjYb07I5vyjQhNUZALdv365PPvnEtfJnfHy85s6dqyVLlmjKlCluLxAAADRMucfKtCY1Q+u3Z6qk3K6O4cG6flJvDe0boQB/2jwbo41p2Xpv5R5V2p2SpPzCCr23co8kEQKBJqLOAFhUVCTDMFyXnU6nSktLJana6SEAAEDTZxiGdh8q0Povd+mntGz5+PgotleYxsZFq0d0K9o8G7mklHRX+Dup0u5UUko6ARBoIuoMgKNHj9aNN96oyy+/XIZh6IsvvtCoUaP0xRdfqF27dp6oEQAAmKy80q6NO7OVnJqhzLwShbQI0GXDOmv0oCi1DWlmdnmoJ/mFFWe1HUDjU2cA/Otf/6qlS5cqOTlZfn5+mj59uhITE7VhwwY98cQTnqgRAACYJKegVKutGfpuR5bKKuzqHNlSf5jcR5eNjNHxY6Vml4d6FhoSeNqwFxoSaEI1ANyhzgBosViUmJioSZMmuVpBjx8/ruHDh7u9OAAA4HlOw9CuA0e1ymrTjvR8WSw+iusVprGDOyqmQ4h8fHyY49dEJcbHVJsDKEkBfhYlxseYWBWA+lRnAPz444/1xBNPqKqqStKJ3n8fHx/t3r3b7cUBAADPKauw6/sdWUpOzVDO0VKFtAjQ1OFdFD8wSm1acgTIG5yc58cqoEDTVWcAfPvtt/Xxxx+rX79+nqgHAAB4WFZ+iVZbM/T9ziyVVzrUrUOIbp7aV4N7hcvfjwXfvM2wfpEEPqAJqzMAtmvXjvAHADhnnFOsYXIahnak5yvZatPOA0fla/HRxX3ClRDXUd06hJhdHgDATeoMgCNGjNA///lPJSQkKDDw/9o/Wrdu7c66AABNAOcUa3hKy6v03fYsrU7N0JFjZWoVHKDLR3ZV/MAotWoRYHZ5AAA3qzMAvvHGG6qsrNTDDz/s2sYcQADAmeCcYg1HRl6JVltt2rAzWxVVDnWPaqXE+G6K7RkmP1/aPAHAW9QZALdv3+6JOgAATRDnFDOX02lo2895WmW1afehAvn5WjSkb7jGxnVU58iWZpcHADBBnQGwsrJSKSkpKikpkSQ5HA79+uuvuuOOO9xeHACgceOcYuYoLqvS+u2ZWpOaobzj5WrTMlAz47tp5IAOCmlOmycAeLM6A+Add9yhw4cPKzc3V3379tW2bdt08cUXe6I2AEAjxznFPMt2pFirrDb9kJatSrtTPTu21uzR3TWoZzv5WmjzBACcQQDcvXu3vv32Wz344IO64YYb5HQ69eCDD3qgNABAY8c5xdzP4XRqy748JVtt2nv4mPz9LBrWL0JjYqPVKYI2T3gnVh8GalZnAAwPD5efn5+6dOmiffv2adKkSSoqKvJEbQCAJoBzirlHUWml1m3L1JotGTpaWKHQkGaaNSpGIwd0UHCQv9nlAaZh9WGgdnUGwObNm+vLL79U7969tXTpUnXr1k2lpaWeqA0AAPzOoewiJVtt+mFXjuwOp/p0bqO5Y3tqYPd2slh8zC4PMB2rDwO1qzMA/u1vf9PSpUt111136bPPPtM111zDAjAAgHpFu1bt7A6nUvflapXVpp9txxXgb9GI/u2VEBulqLBgs8sDGhRWHwZqV2cA7NKli+6++25J0nPPPefuegAAXoZ2rZoVllQqZWuG1mzJ0LHiSoW1bqYrx3TXiP7t1aIZbZ7A6bD6MFC7OgOg1WrVSy+9pPz8fBmG4dr+5ZdfurUwAIB3oF3rVAeyCrVqs02b9uTI7jDUr2tbXTsxWv27hdLmCdSB1YeB2tUZAB944AHNnj1bffr0kY8P/+kAAOoX7Von2B1Obd5zRMlWm9IzCxUY4KtLB3RQQly02oe2MLs8oNFg9WGgdnUGwICAAF1//fUeKAUA4I28vV3rWHGF1m7JUMrWTB0vqVREmyBdNbaHRlzYXkGBdf43DXit2uYOs/owULM6/2fp1q2bduzYoQsvvNAT9QAAvIw3tmsZhqFfMgu1ymrT5j1H5HAa6h8TqoS4aPXr2lYWOm6AWjF3GDh3NQbAqVOnSpJKSkp01VVXqWPHjvLz+7+bMwcQAFAfvKldq8ru1E+7c5RstelgdpGCAn01OjZKCbHRimjb3OzygEaDucPAuasxAD7wwAOerAMA4MWaertWQVGF1myxKWVrpopKq9Q+tLmuGd9Tw/pFKijQTxvTsvX0v7Y0+QAM1BfmDgPnrsYAePHFF6ugoEBOp1OhoaGSpI0bN6pXr15q27atxwoEAKAxMgxD+23HlWy1ybo3V4ZhaED3dkqIi1bfLm1cC6vRygacPW+fOwycD0tNV+zfv1+TJk1Samqqa9t///tfTZs2Tb/88otHigMAoLGprHJo/bZMPfSPTXryo1SlHTiqcRdF64k/DtOCK/qrX9e21VbVrq2VDcDpJcbHKMCv+tfYpj53GKgvNR4BfOaZZ3Tfffdp3Lhxrm1/+9vfdMEFF+ipp57Sq6++6pECAQBoDPKPl2v1FpvWb8tScVmVotq10LUTe2lY30gFBvjWfD9a2YCz5k1zh4H6VmMAzMzMdC0E81uJiYl655133FoUAACNgWEY2vvrMSVbbUrdnytJGtQjTAlx0erdqfUZnT+XVjbg3DT1ucOAu9QYAH19a/610t/f3y3FAADQGFRUObQxLVurrTbZckvUopmfJg7ppNGDotSuVdBZPZY3ngZDqv0cbgAA96kxAIaGhmr37t3q06dPte27du1SUNDZ/ecGAEBTkHusTGtSM7R+e6ZKyu3qGB6sGyb11pC+EQrwr/mH09p4YysbC98AgHlqDIC33XabbrvtNs2fP1+DBg2SYRjasmWLXnnlFT366KOerBEAANMYhqFdhwqUvNmmbT/nycfHR7G9wjQ2Llo9oludUZtnXbytlY1zuAGAeWoMgLGxsVq8eLFefPFFPf7447JYLBo4cKCeeuopDR482JM1AgDgceWVdm3cma1VVpuy8kvVsrm/Jl/SWaMGRqltSDOzy2vUWPgGAMxTYwCUpIsuukjvv/++p2oBAMB0OQWlWm3N0Hc7slRWYVfnyJb6w+Q+urhPuPz9zq3NE9Wx8A0AmKfWAAgAgDdwGobSDhxVstWmHen5slh8NLh3uBLiohXTIaRe2jzxf7x14RsAaAjcGgCLi4s1Z84cvfbaa4qOjtaGDRv0xBNPqKKiQpMmTdIdd9xxyn0yMzN11113KT8/X127dtXTTz+tFi1auLNMAICXKquw6/sdWUpOzVDO0VKFtAjQ1OFdFD8wSm1acjTKXbxx4RsAaCjcFgC3bdum+++/XwcPHpQklZeX695779UHH3yg9u3b69Zbb1VKSori4+Or3e+hhx7S3LlzNXnyZL388st65ZVXdNddd7mrTACAF8rKLznR5rkzSxWVDnXrEKKbp/bVRb3D5edrMbs8r+BtC98AQENxRgEwIyNDx48fl2EYrm39+vWr9T5Lly7VokWLdPfdd0uStm/frs6dO6tjx46SpKlTp+qbb76pFgCrqqq0adMmvfzyy5JOnHT+mmuuIQACAM6b0zC0PT1fyVab0g4cla/FRxf3idDYwdHq2j7E7PIAAPCIOgPg888/r3feeUehoaGubT4+PkpOTq71fo899li1y0eOHFFYWJjrcnh4uHJycqrdpqCgQMHBwfLzO1FWWFjYKbcBAOBslJZX6bvtWVqdmqEjx8rUOjhAl4/sqviBUWrVIsDs8gAA8Kg6A+Dnn3+ub7/9VhEREef1RE6ns9okesMwTplUf7pt5zLxPjQ0+NyKxHkLC2tpdglei7E3F+NvnprG/tfsQn313QGtsR5WeaVDfbq01fVT+mlY//a0edaThv6+X2s9rPdX7lZeQZnatQnStZP6aFRcR7PLqhcNfeybMsbePIx9/agzALZv3/68w58kRUZGKjc313U5NzdX4eHh1W7Ttm1bFRUVyeFwyNfX97S3ORP5+cVyOo26b4h6FRbWUrm5RWaX4ZUYe3Mx/ub5/dg7nYa2/ZynVVabdh8qkJ+vRUP6hmtsXEd1jjzxxaHgaIlZ5TYpDf19vzEtu9pKo7kFZXpx6VYVFpU3+rmHDX3smzLG3jyM/ZmzWHxqPSBWZwAcNmyYFi9erISEBDVr9n8nvq1rDuDvDRgwQAcOHNChQ4cUHR2tr776SjNnzqx2G39/fw0ePFhff/21pk6dqhUrVujSSy89q+cBAHif4rIqrd+eqTWpGco7Xq42LQM1M76bRg7ooJDmtHl6o6SU9GqnmZCkSrtTSSnpjT4AAsD5qDMAJiUlSZK++eYb17YzmQP4e4GBgXryySf15z//WRUVFYqPj9fEiRMlSffdd5/GjBmjhIQELVq0SAsXLtSrr76q9u3ba8mSJWf1PAAA73Ewq1Cf/nevfkjLVqXdqV4dW2v26O4a1LOdfC20eXqz051ovrbtAOAtfIzfLu3ZRNACag4OzZuHsTcX4+9ZDqdTW/blKdlq097DxxTgZ9HQfhFKiOuojuHMAfeUhv6+v+uV708b9kJDAvXUbcNNqKj+NPSxb8oYe/Mw9mfuvFtAS0tLtXjxYq1bt052u13Dhw/Xfffdp+Bg/pMFAHhOUWml1m3L1JotGTpaWKHQkGa6YUpfDYoJVXCQv9nloYFJjI+pNgdQkgL8LEqMjzGxKgAwX50B8IknnpDD4dDLL78sh8Ohf/7zn3rkkUf097//3RP1AQC83KHsIiVbbfphV47sDqf6dG6juWN7amD3doqICOEXYZzWyXl+SSnpyi+sUGhIoBLjY5j/B8Dr1RkAt23bpi+++MJ1+dFHH9XkyZPdWhQAwLvZHU6l7svVKqtNP9uOK8DfohH92yshNkpRYXSg4MwM6xdJ4AOA36kzADocDjmdTln+32R6p9MpX19ftxcGAPA+x0sqlbI1Q2u3ZOhYcaXCWjfTnDHdNaJ/ezVvRpsnAADn64xOA/GXv/xFV111lSTp448/1pAhQ9xeGADAexzIKtSqzTZt2pMju8NQv65tde3EaPXvFiqLxcfs8gAAaDLqDIALFy7UK6+8oiVLlsjhcGjkyJG67bbbPFEbAKAJszuc2rzniFZZbfols1CBAb6KHxClMXFRah/awuzyAABokuoMgH5+flqwYIEWLFjgiXoAAE3cseIKrd2SoZStmTpeUqmINkG6amwPjbiwvYIC6/xvCQAAnIca/6e96qqr9PHHH2vQoEHy8Tm1/SY1NdWthQEAmg7DMJSeWahkq02b9xyRw2mof0yoEuKi1a9rW1lO8/8MAACofzUGwOeff16S9NVXX51yXRM8dzwAwA2q7E79tDtHq6w2HcouUlCgr8bERmtMXJQi2jQ3uzwAALxOjQEwPDxckrRo0SK99dZb1a6bPXu2li5d6t7KAOA8bEzL5vxfJjpaWK61W0+0eRaVVql9aHNdM76nLrkgUs0CaPMEAMAsNf4vvGDBAh04cECHDx/W1KlTXdvtdrsCAgI8UhwAnIuNadl6b+UeVdqdkqT8wgq9t3KPJBEC3cgwDO23Hdcqq02pe3NlGIYGdG+nhMHR6tu5zWmnEwAAAM+qMQDefffdysjI0AMPPKAHHnjAtd3X11fdu3f3SHEAcC6SUtJd4e+kSrtTSSnpBEA3qKxy6MddOUq22vTrkWI1D/TTuIuiNTo2WuGtg8wuDwAA/EaNATA6OlrR0dG68MILdfHFF3uyJgA4L/mFFWe1Hecm/3i5Vm+xaf22LBWXVSkqrIWundhLw/pGKjDA1+zyAADAadQ5EWP//v0yDIPWHQCNRmhI4GnDXmhIoAnVNC2GYWjvr8e0ymrTlv25kqRBPcI0Ni5avTq15v8KAAAauDoDYFhYmCZPnqwBAwaoRYv/OzHv/fff79bCAOBcJcbHVJsDKEkBfhYlxseYWFXjVlHp0MZd2Uq22pSRW6IWzfw0cUgnjR4UpXataPMEAKCxqDMADho0SIMGDfJELQBQL07O82MV0POXe6xMq1NPtHmWVtjVKTxYN0zqrSF9IxTgT5snAACNTZ0B8Pbbb1dJSYnS0tJkt9vVv39/BQcHe6I2ADhnw/pFEvjOkWEY2nWoQMmbbdr2c558fHwU2+tEm2eP6Fa0eQIA0IjVGQC3b9+u2267Te3atZPD4VBOTo5ee+01xcbGeqI+AICHlFfatWHniTbPrPxStWzur8mXdNaogVFqG9LM7PIAAEA9qDMA/v3vf9fTTz+toUOHSpI2btyoJ598khPBA0ATkVNQqtXWDH23I1NlFQ51jmypP0zuo4v7hMvfjzZPAACakjoDYElJiSv8SdKwYcP0+OOPu7UoAIB7OQ1DaQeOKtlq0470fFksPhrcO1wJcdGK6RBCmycAAE1UnQHQx8dHGRkZioqKkiTZbDb5+vKLMAA0RmUVdn23I0urrTblFJQppEWApg7volGDotQ6mNNkAADQ1NUZAOfPn68rr7xSw4YNkyR9//33WrRokdsLAwDUn6z8khNtnjuzVFHpULcOIbp5RFdd1Dtcfr4Ws8sDAAAeUmcAHDt2rLp166YffvhBhmHoj3/8o2JiOJcWADR0TsPQ9vR8JVttSjtwVH6+Prqod4TGDo5W1/YhZpcHAABMUGcAlKTDhw/rl19+ka+vr7p3704ABIAGrLS8Suu3Z2l1qk25x8rVOjhAM0Z21aUDo9SqRYDZ5QEAABPVGQBffPFFff3115o4caKcTqf+9re/6eqrr9a1117rifoAAGcoI69EyVabNuzMUmWVU92jW2lmfIxie4bR5gkAACSdQQD84osvlJSUpJYtW0qSbrzxRs2ZM4cACAANgNNpaOvPeUq22rT7UIH8fC0a2jdCCXHR6hzZ0uzyAEjamJatpJR05RdWKDQkUInxMRrWL9LssgB4qToDYOvWrdWiRQvX5ZCQEDVv3tytRQEAaldcVqX12zO12pqh/MJytWkZqJnx3XTpgA5q2Zw2T6Ch2JiWrfdW7lGl3SlJyi+s0Hsr90gSIRCAKeoMgHFxcbrtttt05ZVXytfXV1988YU6dOigb7/9VpI0fvx4txcJADjh8JFiJVsP64e0HFXanerVsbWuHNNdg3q2k6+labV5ctQETUFSSror/J1UaXcqKSWd9zMAU9QZANPS0iRJ77zzTrXtH3zwgXx8fAiAAOBmDqdTW/blaZXVpn2HjynAz6Kh/SKVEBetjuHBZpfnFhw1QVORX1hxVtsBwN3qDIAffPCBJMlut8swDPn7+7u9KACAVFRaqXXbMrVmS4aOFlYoNKSZZo2O0cj+HRQc1LT3xRw1QVMRGhJ42rAXGhJoQjUAcAYBMD8/X3/961/1ww8/yOFw6KKLLtJTTz2liIgIT9QHAI3aubQxHsou0irrYf2464jsDqf6dG6jq8f21IDu7WSx+HiocnNx1ARNRWJ8TLWj2ZIU4GdRYjyn1AJgjjoD4MMPP6yBAwdqyZIlcjgc+uCDD/Tggw/q1Vdf9UR9ANBonU0bo93hVOq+XK2y2vSz7bgC/C0a0b+9EmKjFBXWNNs8a8NREzQVJz/rzGcF0FDUGQAPHjyo559/3nV5wYIFmjx5sluLAoCm4EzaGI+XVCpla4bWbsnQseJKhbVupjljumtE//Zq3qxpt3nWhqMmaEqG9Ysk8AFoMOoMgHa7XRUVFQoMPPGra1lZmXx8vKMFCQDOR21tjAeyCrVq82Ft2nNEdoehfl3b6rqJ0bowJlQW9rEcNQEAwE3qDICXXXaZrr/+eiUmJsrHx0fLli3ThAkTPFEbADRqNbUx+vn66JH3NiswwFfxA6I0Ji5K7UNbnOYRmp6zmRPJURMAAOpfnQFw/vz5ioyM1Pr16+V0OpWYmKgrrrjCE7UBQKN2ujZGSWrRzF+Th3XW8AvbKyiwzt1wk8GpHQAAMF+d3zyuu+46vffee5o5c6Yn6gGAJsEwDIW1DlLHiGClZxRKkvz9LEqIi9YVo2K8ss2TUzsAAGC+OgNgUVGRSktL1bx5c0/UAwCNWpXdoZ92H9Eqq02HsosUFOircYM7akxclCLaePd+lFM7AABgvjoDYFBQkEaPHq1evXpVC4GvvfaaWwsDgMbkaGG51mzJ0LptmSoqrVL70OaaN76nhl0QqWYB3tPmWRtO7QAAgPnq/FbCfD8AOD3DMLTfdlxvr9yjjduzZBiGBnRvp4TB0erbuQ0rJv8Op3YAAMB8tQbAffv2qUWLFhowYIAiIiI8VRMANGiVVQ79sCtHyVabDh8pVosgf42/qKNGx0YprHWQ2eU1WJzaAQAA89UYAJctW6a///3v6ty5s3799Vc988wzGjFihCdrA4AGJf94uVZvsWnd1kyVlNsVFdZC107spamXdldRYZnZ5TUKnNoBAABz1RgAP/jgA3355ZeKiIjQli1b9OyzzxIAAXgdwzC059djSrbatGV/riQptkeYEuKi1atTa/n4+KhZoJ+KTK4TAADgTNTaAnqy7XPQoEEqKCjwSEEA0BBUVDq0cVe2kq02ZeSWqEUzP00c0kmjB0WpXSvaPAEAQONUYwD8/eIFvr6+bi8GAMyWe6xMq1NtWr8tS6UVdnUKD9YNk3prSN8IBfizHwQAAI3bGa9Nzmp2AJoqwzC062CBkq02bfs5Tz4+PorrdaLNs0d0K/Z/AACgyagxAO7du1exsbGuy+Xl5YqNjZVhGPLx8VFqaqpHCgQAdymvtGvDzhNtnln5pWrZ3F+TL+msUQOjtPfwMb35ZRqrVQKN0Ma0bFabBYAa1BgA//vf/3qyDgDwmJyCUiVbbfp+R5bKKhzqEtlSf5jcRxf3CZe/n682pmVXO19dfmGF3lu5R5L4Egk0cHx+AaB2NQbAqKgoT9YBAG7lNAzt/OWokq027fglX74WH13UO1wJcdHq1iGkWptnUkp6tZOVS1Kl3amklHS+QAINHJ9fAKjdGc8BBIDGqKzCru92ZGm11aacgjKFtAjQtOFdNGpQlFoHB572PvmFFWe1HUDDwecXAGpHAATQJGXll5xo89yZrYpKh2I6hGj6iK4a3Dtcfr6WWu8bGhJ42i+LoSGnD4wAGg4+vwBQOwIggCbD6TS0/Zd8JVttSjtwVH6+Prqod4TGDo5W1/YhZ/w4ifEx1eYQSVKAn0WJ8THuKBtAPeLzCwC1IwACaPRKy6u0fnuWVqfalHusXK2DAzRjZFfFD4xSSIuAs368k/OEWEUQaHz4/AJA7QiAABqtjNxiJadmaMPOLFVWOdU9upVmxscotmdYnW2edRnWL5IvjEAjxecXAGpGAATQqDidhrb+nKdkq027DxXIz9eioX0jlBAXrc6RLc0uDwAAoEEjAAJoFIrLqrR+W6ZWp2Yov7BcbUMCNTO+my4d0EEtm599mycaPk7mDQBA/SMAAmjQDh8pVrL1sDam5ajK7lSvjq115ZjuGtSznXwt59fmiYaLk3kDAOAeBEAADY7D6dSWfXlaZbVp3+FjCvCzaFi/SCXERatjeLDZ5cEDOJk3AADuQQAE0GAUllZq3dZMrdmSoYKiCrVr1UyzR3fXiP7tFRzkb3Z58CBO5g0AgHsQAAGY7lB2kVZZD+vHXUdkdzjVp3MbXTOupwZ0byeLxcfs8mACTuYNAIB7EAABmMLucCp1X65Wbbbp54zjCvT31cj+7TUmLlpR7VqYXR5Mxsm8AQBwDwIgAI86XlKplK0ZWrslQ8eKKxXeOkhzxpxo82zejDZPnMDJvAEAcA+PB8BPP/1UH374oeuyzWbT9OnT9be//c217aWXXtKyZcsUEhIiSZo9e7auvvpqT5cKoB79klmoZOthbdpzRHaHoQu6ttV1E6N1YUyoLD60eeJUnMwbAID65/EAOGvWLM2aNUuStH//fs2fP1+33357tdvs3LlTS5Ys0aBBgzxdHoB6ZHc4tWnPEa3abNOBrEIFBvgqfkCUxsRFqX0obZ4AAACeZmoL6IMPPqg77rhDbdu2rbZ9586dev3115WRkaGLLrpIf/3rXxUYyMR/oLE4VlyhtVsytHZrpgpLKhXRtrnmju2h4Re2V1AgnecAAABm8TEMwzDjiTds2KBnnnlGy5Ytq7a9pKREf/nLX7Rw4UJ17txZCxcuVFRUlO644w4zygRwhgzD0J6DBfrqu1/0/fZMOQ1Dcb0jNHVENw3sGcZqngAAAA2AaQFwwYIFGj9+vKZMmVLr7Xbt2qV7771XK1asOOPHzs8vltNpyp/l1cLCWio3t8jsMrySmWNfZXfop91HtMpq06HsIgUF+mrEhR00Ji5KEW2am1KTp/HeNw9jbx7G3jyMvXkYe/Mw9mfOYvFRaGhwjdeb0otVWVmpTZs26cknnzzluszMTG3YsEFXXHGFpBNHFfz8aBkDGpqjheVasyVDKVszVVxWpfahzTVvfE8NuyBSzQL4zAIAADREpnxL27t3r7p06aLmzU89OtCsWTM99dRTGjJkiKKjo/XRRx9p3LhxJlQJ4PcMw9B+23Gt2nxYqfvyZBiGBnRvp7GDo9Wncxv5sJonAABAg2ZKADx8+LAiI6sv7X3zzTdrwYIFuvDCC/Xwww/rT3/6k6qqqhQbG6sbbrjBjDIB/D+VVQ79sCtHyVabDh8pVvNAP42/qKNGx0YprHWQ2eUBAADgDJk2B9CdmANoDnqzzeOusc87XqY1qRlaty1TJeV2RYe1UEJctIb2i1Sgv2+9P19jxXvfPIy9eRh78zD25mHszcPYn7kGOQcQQMNlGIb2/HpMyVabtuzPlSTF9gjT2MHR6tmxNW2eAAAAjRgBEIAkqaLSoY1p2UpOtSkjt0TBQf6aNKSzRg+KUmirZmaXBwAAgHpAAAS83JFjZVqTatP6bVkqrbCrU3iwbpjUW0P6RiiANk8AAIAmhQAIeCHDMLTrYIGSrTZt+zlPPj4+iusVpoS4aPWIbkWbJwAAQBNFAAS8SFmFXRt2Zmt1qk1Z+aVq2dxfky/potGDotSmZaDZ5QEAAMDNCICAF8g5WqrkVJu+35GlsgqHukS21B8m99HFfcLl70ebJwAAgLcgAAJNlNMwtPOXo0q22rTjl3z5Wnx0Ue9wJcRFq1uHENo8AQAAvBABEGhiyirs+m5HllZbbcopKFOrFgGaPqKr4gd2UOtg2jwBAAC8GQEQaCKy8kuUbLXp+53Zqqh0KKZDiKaP6KrBvcPl52sxuzwAAAA0AARAoBFzOg1t/yVf65J2aOu+XPn5+ujiPhFKiItW1/YhZpcHAACABoYACDRCpeVVWr89S6tTbco9Vq62Ic00Y2RXxQ+MUkiLALPLAwAAQANFAAQakYzcYiVbbdqQlq3KKqd6RLfSzPgYTRjeTQVHS8wuDwAAAA0cARBo4JxOQ1t/zlOy1abdhwrk52vR0L4n2jw7R7aUJOb4AQAA4IwQAIEGqrisSuu3ZWp1aobyC8vVNiRQM+O76dIBHdSyOW2eAAAAOHsEQKCB+TWnSMlWm37YlaMqu1O9O7XWnITuGtijnXwtHOkDAADAuSMAAg2Aw+nUln15WmW1ad/hYwrws2hYv0glxEWrY3iw2eUBAACgiSAAAiYqLK3Uuq2ZWrMlQwVFFWrXqplmj+6uEf3bKzjI3+zyAAAA0MQQAAETHMwuVPJmm37cfUR2h1N9OrfRNeN7akBMO1ksPmaXBwAAgCaKAAh4iN3hlHVvrpKtNv2ccVyB/r4a2b+9xsRFK6pdC7PLA4AmbWNatpJS0pVfWKHQkEAlxsdoWL9Is8sCAI8jAAJudrykUilbM7RmS4aOF1cqvHWQ5iT00IgLI9W8GW2eAOBuG9Oy9d7KPaq0OyVJ+YUVem/lHkkiBALwOgRAwE1+ySxUsvWwNu05IrvD0AVd2+r6idG6MCZUFh/aPAHAU5JS0l3h76RKu1NJKekEQABehwAI1KMqu1Ob9xzRKqtNB7IK1SzAV/EDozQmNkrtQ2nzBAAz5BdWnNV2AGjKCIBAPSgoqlDK1gyt3ZqpwpJKRbRtrrlje2j4he0VFMjHDADMFBoSeNqwFxoSaEI1AGAuvpkC58gwDKVnFGqV9bCse3PldBq6MCZUY+Oi1bdrW9o8AaCBSIyPqTYHUJIC/CxKjI8xsSoAMAcBEDhLVXaHftx1RMlWmw7lFCko0FcJcdEaHRuliDbNzS4PAPA7J+f5sQooABAAgTN2tLBca7ZkKGVrporLqtQ+tLnmje+pYRdEqlkAHyUAaMiG9Ysk8AGACIBArQzD0L7Dx5RstSl1X54Mw9DAHu2UEBetPp3byIc2TwAAADQiBEDgNCqrHPphV46SrTYdPlKsFs38NP6ijhodG6Ww1kFmlwcAAACcEwIg8Bt5x8u0JjVD67ZlqqTcruiwFrpuYi8N7RepQH9fs8sDAAAAzgsBEF7PMAzt+fWYVm0+rK0/50mSYnuGaWxctHp2bE2bJwAAAJoMAiC8VkWlQxvTspWcalNGbomCg/w1aUhnjR4UpdBWzcwuDwAAAKh3BEB4nSPHyrTaatN327NUWmFXp4hg3XBZbw3pE6EA2jwBAADQhBEA4RUMw9CugwVKttq07ec8+fj4KK5XmBLiotUjuhVtngAAAPAKBEA0aWUVdm3Yma3VqTZl5ZeqZXN/Tb6ki0YPilKbloFmlwcAAAB4FAEQTVLO0VIlW236fmeWyioc6hLZUjdN6aOLekfI389idnkAAACAKQiAaDKchqGdvxxVstWmHb/ky9fio4t6hyshLlrdOoTQ5gkAAACvRwBEo1dabtf3O7K0OtWmnIIytWoRoOkjuip+YAe1DqbNEwAAADiJAIhGKzOvRMmpNm3Yma2KSodiOoRo+siuGtwrXH6+tHkCAAAAv0cARKPidBranp6vZOthpR0skJ+vjy7uE6GEuGh1bR9idnkAAABAg0YARKNQUl6l9duytGaLTbnHytU6OEAzRnZV/MAohbQIMLs8AAAAoFEgAKJBs+UWa7XVpg1p2aqscqpHdCvNjI9RbM8w2jwBAACAs0QARIPjdBrasj9PydbD2vPrMfn5WjS0X4QSYqPVObKl2eUBAAAAjRYBEA1GcVmV1m/L1OrUDOUXlqttSKBmxnfTpQM6qGVz2jwBAACA80UAhOl+zSlSstWmH3blqMruVO9OrTUnobsG9mgnXwttngAAAEB9IQDCFA6nU6n78pS8+bD22Y4rwM+iSy6IVEJstKLDg80uDwAAAGiSCIDwqMLSSq3bmqk1WzJUUFShdq2aafbo7hrRv72Cg/zNLg8AAABo0giA8IiD2YVK3mzTj7uPyO5wqm+XNrpmfE8NiGkni8XH7PIAAAAAr0AAhNvYHU5Z9+ZqlfWw0jMKFejvq5H922tMXLSi2rUwuzwAAADA6xAAUe+Ol1QqZUuG1mzN0PHiSoW3DtKchB4acWGkmjejzRMAAAAwCwEQ9WbvoaP6LHmfNu0+IofT0AVd2+r6idG6MCZUFh/aPAEAAACzEQBxXqrsTm3ec0SrrDYdyCpUswBfjRoUpTGxUWofSpsnAAAA0JAQAHFOCooqtHZLhlK2ZqiwtEqRbZvr1hkXqn+XNgoK5G0FAAAANER8U8cZMwxD6RmFWmU9LOveXDmdhi6MCdXYuGj17dpWEeEhys0tMrtMAAAAADUgAKJOVXaHftx1RMlWmw7lFCko0E8JcdEaHRuliDbNzS4PAAAAwBkiAKJGRwvLtWZLhlK2Zqq4rEod2rXQvAm9NKxfhJoF8NYBztbGtGwlpaQrv7BCoSGBSoyP0bB+kWaXBQAAvAjf4lGNYRjad/iYkq02pe7Lk2EYGtijnRLiotWncxv5sJoncE42pmXrvZV7VGl3SpLyCyv03so9kkQIBAAAHkMAhCSposqhH3flKNlq0+EjxWrRzE/jL+6oMYOi1K51kNnlAY1eUkq6K/ydVGl3KiklnQAIAAA8hgDo5fKOl2lNaobWbctUSbld0WEtdN3EXhraL1KB/r5mlwc0GfmFFWe1HQAAwB0IgF7IMAztOVSgVVabtv6cJ0mK7RmmsXHR6tmxNW2egBuEhgSeNuyFhgSaUA0AAPBWBEAvUlHp0Ia0bK222pSRV6LgIH9NGtJZowdFKbRVM7PLA5q0xPiYanMAJSnAz6LE+BgTqwIAAN6GAOgFjhwr02qrTd9tz1JphV2dIoJ1w2W9NaRPhAJo8wQ84uQ8P1YBBQAAZiIANlGGYSjt4FElb7Zpe3q+fHx8NLh3mBLiotU9qhVtnoAJhvWLJPABAABTmRIA582bp6NHj8rP78TTP/zwwxowYIDr+t27d+u+++5TSUmJBg8erIceesh1W9SurMKuDTuzlWy1KftoqVo299fkS7po9KAotWnJXCMAAADAm3k8VRmGoYMHD2rNmjU1hrq77rpLjz76qAYOHKh7771XS5cu1dy5cz1caeOSc7RUyVabvtuRpfJKh7q2b6mbpvTRRb0j5O9nMbs8AAAAAA2AxwPgL7/8Ikm68cYbdezYMc2ePVvXXHON6/qMjAyVl5dr4MCBkqTExES98MILBMDTcBqGdv5yVKush7Xzl6Pytfjoot7hShgcrZgOrcwuDwC8xsa0bOZ3AgAaBY8HwMLCQg0bNkwPPPCAqqqqdO2116pr164aPny4JOnIkSMKCwtz3T4sLEw5OTln9RyhocH1WnNDU1JWpeRNv+rf3x9QZl6J2rQM1NzxvTRxWBe1CTF3Nc+wsJamPr83Y+zNxfibx+yxX2s9rPe/2auKKoekE+d2fP+bvQpp2Uyj4jqaWpu7mT323oyxNw9jbx7Gvn54PAAOGjRIgwYNcl2+4oorlJKS4gqATqez2gIlhmGc9YIl+fnFcjqN+im4AcnMK1Fyqk0bdmarotKhmKgQ3TKtrwb3Cpefr0X2iirl5laZVl9YWEvl5haZ9vzejLE3F+NvntONvaePxr37VZor/J1UUeXQu1+lqV+n1m57XrPxvjcPY28ext48jP2Zs1h8aj0g5vEAuHnzZlVVVWnYsGGSTgS8384FjIyMVG5urutyXl6ewsPDPV1mg+F0Gtqenq9k62GlHSyQn6+PLu4ToYS4aHVtH2J2eQDQoGxMy652vsX8wgq9t3KPJLktBOYXVpzVdgAAzOTxAFhUVKQXXnhB//rXv1RVVaXly5froYcecl0fFRWlwMBAWa1WxcXF6fPPP9ell17q6TJNV1JepfXbsrQ61aa84+Vq0zJQMy7tpvgBHRTSIsDs8gCgQUpKSXeFv5Mq7U4lpaS7LQCGhgSeNuyFhrDyMgCg4fF4ABw9erS2bdumyy+/XE6nU3PnztWgQYN08803a8GCBbrwwgv19NNP6/7771dxcbH69euna6+91tNlmsaWW6xkq00b07JVWeVUj+hWumJUjGJ7hsnPl9U8AaA2ZhyNS4yPqXbUUZIC/CxKjI9x23MCAHCuTDm53l/+8hf95S9/qbbtzTffdP27d+/e+uyzzzxclXkcTqe27j/R5rnn12Py97NoSN8IjY2LVqcIJrsCwJky42jcySOLrAIKAGgMOLu6iYrLqrRuW6bWpNqUX1ihtiGBumJUjC4d0EHBQf5mlwcAjY5ZR+OG9Ysk8AEAGgUCoAl+zSlSstWmH3blqMruVO9OrTUnoYcG9mgnXwttngBwrjgaBwBA7QiAHmJ3OLVlf56SNx/WPttxBfhZdMkFkUqIjVZ0eNM+byEAeBJH4wAAqBkB0ANsR4r17KfbVFBUoXatmmn26O4a0b89bZ4AAAAAPIoA6CG9O7XW4N7hGhDTThbL2Z3YHgAAAADqAwHQA6LDg3Xz1H5mlwEAAADAy7HiCAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CQIgAAAAAHgJAiAAAAAAeAkCIAAAAAB4CT8znvSll17SypUrJUnx8fG6++67T7l+2bJlCgkJkSTNnj1bV199tcfrBNC0bUzLVlJKuvILKxQaEqjE+BgN6xdpdlkAAABu4/EAuGHDBn333Xdavny5fHx8dNNNN+m///2vxo0b57rNzp07tWTJEg0aNMjT5QHwEhvTsvXeyj2qtDslSfmFFXpv5R5JIgQCAIAmy+MtoGFhYVq4cKECAgLk7++vmJgYZWZmVrvNzp079frrr2vq1Kl6+OGHVVFR4ekyATRxSSnprvB3UqXdqaSUdJMqAgAAcD+PB8AePXpo4MCBkqSDBw9q5cqVio+Pd11fUlKiPn366K677tLy5ctVWFioV155xdNlAmji8gtP/8NSTdsBAACaAh/DMAwznnj//v269dZb9ec//1kzZsyo8Xa7du3SvffeqxUrVniuODQ5a62H9f7K3corKFO7NkG6dlIfjYrraHZZMNGNj36r3IKyU7aHtQnSO/ePN6EiAAAA9zNlERir1aoFCxbo3nvv1eTJk6tdl5mZqQ0bNuiKK66QJBmGIT+/syszP79YTqcpudarhYW1VG5ukdllnOL3c71yC8r04tKtKiwqbzJzvRrq2Ddkl4/oWu19IUkBfhZdPqLrWY8l428ext48jL15GHvzMPbmYezPnMXio9DQ4Bqv93gAzMrK0vz58/Xss89q2LBhp1zfrFkzPfXUUxoyZIiio6P10UcfVVsgBjhbtc31aioBsLFpCKtvnnw+s+sAAADwJI8HwLffflsVFRV68sknXdvmzJmj1atXa8GCBbrwwgv18MMP609/+pOqqqoUGxurG264wdNloglhrlfD0pBW3xzWL5LABwAAvIrHA+D999+v+++//5TtV111levfEyZM0IQJEzxZFpqw0JDA04a90JBAE6oBR2QBAADMY8ocQMCTEuNjTjvXKzE+xsSqvBdHZAHv0BBavQEApyIAosljrlfDwhFZoOlrSK3eAIDqCIDwCsz1ajg4Igs0fbR6A0DDRQAE4FEckQWaPlq9AaDhIgAC8DiOyAJNG63eANBwWcwuAAAANC2J8TEK8Kv+FYNWbwBoGDgCCAAmYIVENGW0egNAw0UABAAPY4VEeANavQGgYaIFFAA8rLYVEgEAANyJAAgAHsYKiQAAwCwEQADwsJpWQmSFRAAA4G4EQADwMFZIBAAAZmERGADwMFZIBAAAZiEAAoAJWCERAACYgRZQAAAAAPASBEAAAAAA8BIEQAAAAADwEgRAAAAAAPASBEAAAAAA8BIEQAAAAADwEgRAAAAAAPASBEAAAAAA8BIEQAAAAADwEgRAAAAAAPASBEAAAAAA8BIEQAAAAADwEgRAAAAAAPASBEAAAAAA8BIEQAAAAADwEgRAAAAAAPASBEAAAAAA8BIEQAAAAADwEn5mF+AOFouP2SV4LcbePIy9uRh/8zD25mHszcPYm4exNw9jf2bqGicfwzAMD9UCAAAAADARLaAAAAAA4CUIgAAAAADgJQiAAAAAAOAlCIAAAAAA4CUIgAAAAADgJQiAAAAAAOAlCIAAAAAA4CUIgAAAAADgJQiAAAAAAOAlGlUA/PLLL3XZZZdp/Pjx+uijj065/qWXXtLo0aM1ffp0TZ8+3XWbzMxMXX311Zo4caL+9Kc/qaSkxNOlN3q1jf3u3btdYz59+nSNHDlSU6ZMkSQtX75cI0aMcF337LPPmlF+o1dcXKwpU6bIZrOdct3u3buVmJioCRMm6L777pPdbpfE+76+1Db2q1at0vTp0zVt2jTddtttOn78uCTe9/WltrFnf+9eNY09+3v3eumllzR58mRNnjxZixcvPuV69vfuVdf4s893n7rGnn1+PTMaiezsbGP06NFGQUGBUVJSYkydOtXYv39/tdvceuutRmpq6in3veWWW4yvvvrKMAzDeOmll4zFixd7pOam4kzG/qTS0lJj8uTJxqZNmwzDMIyHH37Y+PLLLz1ZbpOzdetWY8qUKUa/fv2Mw4cPn3L95MmTjS1bthiGYRj33HOP8dFHHxmGwfu+PtQ29kVFRcbw4cON7OxswzAM47nnnjMeeeQRwzB439eHut737O/dp66xP4n9ff36/vvvjSuvvNKoqKgwKisrjWuvvdb49ttvq92G/b371DX+7PPd50ze++zz61ejOQK4YcMGDR06VK1bt1bz5s01YcIEffPNN9Vus3PnTr3++uuaOnWqHn74YVVUVKiqqkqbNm3ShAkTJEmJiYmn3A+1O5OxP+n111/XRRddpMGDB0uSduzYoeXLl2vq1Km68847Xb+W4cwtXbpUixYtUnh4+CnXZWRkqLy8XAMHDpT0f+9v3vf1o7axr6qq0qJFixQRESFJ6tWrl7KysiTxvq8PtY29xP7eneoa+5PY39evsLAwLVy4UAEBAfL391dMTIwyMzNd17O/d6+6xp99vvvUNfYS+/z61mgC4JEjRxQWFua6HB4erpycHNflkpIS9enTR3fddZeWL1+uwsJCvfLKKyooKFBwcLD8/PwknXiT/fZ+qFtdY39SUVGRli5dqttvv921LSwsTLfddpu++OILtW/fXg8//LBHam5KHnvsMdcXrN/7/Wtz8v3N+75+1Db2bdq00bhx4yRJ5eXleuONNzR27FhJvO/rQ21jz/7evWob+5PY39e/Hj16uMLdwYMHtXLlSsXHx7uuZ3/vXnWNP/t896lr7Nnn179GEwCdTqd8fHxclw3DqHa5RYsWevPNNxUTEyM/Pz/deOONSklJOeV2kk65jNrVNfYnffHFFxo7dqxCQ0Nd215++WXFxcXJx8dHN910k9avX++Rmr1FTa8N73vPKSoq0i233KLevXtrxowZknjfuxv7e/Oxv3ef/fv368Ybb9Tdd9+tLl26uLazv/eMmsb/JPb57lPT2LPPr3+NJgBGRkYqNzfXdTk3N7dae0pmZqY+++wz12XDMOTn56e2bduqqKhIDofjtPdD3eoa+5NWrVqlyy67zHW5qKhI7777ruuyYRjy9fV1a63e5vevTV5ensLDw3nfe8iRI0c0d+5c9erVS4899pgk3veewP7efOzv3cNqter666/X//7v/7rCxUns792vtvGX2Oe7U21jzz6//jWaAHjJJZdo48aNOnr0qMrKyvTtt9/q0ksvdV3frFkzPfXUUzp8+LAMw9BHH32kcePGyd/fX4MHD9bXX38tSVqxYkW1+6FudY29dOLDmJaWpkGDBrm2NW/eXG+99Za2bdsmSfrwww9d7ROoH1FRUQoMDJTVapUkff7557r00kt533uAw+HQH//4R02aNEn33Xef61dH3vfux/7eXOzv3SMrK0vz58/X008/rcmTJ59yPft796pr/Nnnu09dY88+v/75GIZhmF3Emfryyy/1+uuvq6qqSldccYVuvvlm3XzzzVqwYIEuvPBC/ec//9GLL76oqqoqxcbG6qGHHlJAQIAyMjK0cOFC5efnq3379lqyZIlatWpl9p/TqNQ19vn5+Zo2bZq+//77avfbvHmzHnvsMZWXl6tLly5avHixWrZsadJf0biNGTNG77//vqKjo6uN/Z49e3T//ff//+3dX0hTbxzH8c9ZiiERlYHYtCDSEdo/EfoHQV2MLsrpRVjIGqwYhHURgZSQjNowqotsFBhEeBURSVO6mNgI7A+LEro2IiqoUbbClf3ZOL8LaeCvpjbt96Od9+vy2Tnf55yHwwOfneeco2QyqerqanV0dHDdz7Jfjf2bN2906NAhORyOzHY1NTUKBoNc97Mo23XPfP/nZRt75vs/IxAI6MaNG1q6dGmmbffu3YpGo8z3/4Gpxp85/8+ZzrXPnD+7/qoACAAAAADI3V+zBBQAAAAAMDMEQAAAAACwCAIgAAAAAFgEARAAAAAALIIACAAAAAAWUfB/HwAAADPlcDhUVVUlm80mwzA0NjamefPmye/3a9WqVZPue/36dX379k3Nzc26evWqRkdH5fP5pt232+3Ww4cPNTAwoIqKikx7LBbT3r171draqn379v3W+Xi9Xp09e1aLFi3KtD19+lRHjhyRJH38+FGjo6MqLy+XJDU2Nmr+/PkKBoMqLy+XaZpKpVKqqKjQyZMn+TgyACCDAAgAyAvd3d0TAtPly5cVCAR07dq1Sfd7/PixKisrJUl79uzJqe8lS5YoHA7r4MGDmbabN29q8eLFOdX79zf2JGnFihUKh8OSpJ6eHkUiEXV1dWV+7+npUV1d3YQ2v9+v8+fPKxAI5HQcAID8QwAEAOSdVCql169fZz4I/O7dO7W3t2tkZERv376V3W7XuXPnNDQ0pGg0qnv37mnu3Ll6//69EomE2tvbNTw8rBMnTujDhw8yDENer1cNDQ2/7K++vl59fX2ZADg2NqahoSFt3Lgxs022erFYTMFgUMXFxfr06ZNqamokSR6PR5cuXVJZWVlOY/D9+3clk8kJdyUBACAAAgDygsfjkSQlEgkVFRVp69at6ujokCTdunVLa9eulc/nk2ma8vl8CofD8nq9un37tiorK9Xc3KxQKCRpPEAeOHBAra2tcjqdisfj2rVrl5YtW6Z169b91PfKlSsVjUb15MkTrVmzRv39/dq2bZsSicSU9aTxcDgwMCC73S5p/G7ev+9oTsejR4/kcrlkmqbi8biKiop0+PDh3AYUAJCXeAkMACAvdHd3q6+vT11dXfry5YvWr1+vkpISSePhsLa2VleuXJHf79fw8LA+f/6ctdbz58/19etXOZ1OSVJpaamcTqcGBwez7uNyudTb2ytpfPlnY2PjtOuVlZVlwt9M1NXVKRwOq7e3Vw8ePFBTU5P2798v0zRnXBsAkB8IgACAvFJdXa1jx47p6NGjevXqlSTpzJkz6uzs1MKFC9XU1KTNmzdPGorS6bQMw5jQ9uPFKtns3LlTkUhEL1++VDKZVFVV1bTrFRcX//Z5TsVms8ntduvZs2caGRmZ9foAgL8TARAAkHd27Nih1atXZ5aA3r17Vx6PRw0NDSopKdH9+/eVTqclSXPmzPkp2C1fvlwFBQXq7++XJMXjcUUiEW3atClrn6WlpXI4HGpra5PL5ZpRvV8dUy7u3Lkju93+20tJAQD5i2cAAQB56fjx46qvr9fg4KBaWlp0+vRpdXZ2qrCwULW1tXrx4oUkacuWLTp16tSEfQsLC3Xx4kUFAgGFQiGl02m1tLRow4YNk/bpcrnU1taWeZZwOvVisdhPdbZv3y63261QKDThTuJUfjwDaBiGUqmUFixYoAsXLshm4/9eAMA4w+TBAAAAAACwBP4SBAAAAACLIAACAAAAgEUQAAEAAADAIgiAAAAAAGARBEAAAAAAsAgCIAAAAABYBAEQAAAAACyCAAgAAAAAFvEPRTBleC0TLYUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Studentized Residuals')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3cAAAJdCAYAAACRc47yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABdKElEQVR4nO3deXxU1d3H8e9sySQkhCUBcQE1CLilYNEUoSKoEHZQERCtawUXqriAqLigFkp53PvgUp9abZS6gBYXhGK1CoogCooIkiphJwESkpBkJjPn+YNmJIYsZJmbe/N5v16+ZO5N7vwm514u35xzz3EZY4wAAAAAALbmtroAAAAAAED9Ee4AAAAAwAEIdwAAAADgAIQ7AAAAAHAAwh0AAAAAOADhDgAAAAAcgHAHwBG2bt2qHj16WF1Gk7Z7927dcsstGjZsmIYNG6bRo0frn//8Z2T/U089VeF1bfXo0UNbt26tc12vvfaaMjMzJUmvvPKKnn322Tof61Bff/21+vfvf9h9/fv318CBAzVixAiNHDlSgwcP1tChQ/Xvf/+7zu/3+OOP680336y0fe/everatWudjytJXbt21d69e+v8/Yd+3mHDhmnIkCH6+9//Htk/YsQI7d+/v141lrv77ru1fPnyar9my5YtmjRp0hEf+/LLL1f//v01YsSIyGcZOHDgYX/uTcGuXbs0duxYq8sA0Ix4rS4AABAd99xzj84++2w99thjkqRNmzZp3LhxOuGEE5SamqoVK1aoc+fOUa/riy++0EknnSRJGjduXNTed86cOTr99NMjrxctWqS77rpLn3zySZ2Od/PNNzdUaY3i0M+7Y8cODRw4UOecc446dOigt956q8He5+GHH67xa7Zv364ffvihTsefMmWKMjIyIq+//vprjRs3Tueff74SEhLqdMzG0r59e82bN8/qMgA0I4Q7AI4XCAQ0Z84crVy5UqFQSKeccoruueceffXVV/rDH/6ghQsXSpL279+v8847T//85z9VUlKiGTNmaMeOHQoGgxoyZIgmTpyorVu3avz48UpNTdW2bdv00ksvaf78+Vq6dKlKSkpUXFysqVOn6oILLlBxcbHuu+8+rVmzRomJiZHgNGvWLO3ateuwxz/UDz/8oLFjx+rjjz9WTEyMQqGQzj33XL3wwgvKysrS3Llz5XK55PF4NGXKFJ155pnV/hxycnJUUlKicDgst9utzp07a+7cuWrZsqUyMzP1zTffaPbs2fJ4PFq6dKlOOukkXXPNNZKkO++8M/J61apVevDBB+VyuXT66acrHA5H3uODDz7Q3LlzFQwG5ff7NXXqVPXo0UNPPvmktm3bppycHG3btk3t27fXH//4R61Zs0YffPCBli1bJr/fr71792rfvn367W9/W+HnkZubK6/Xq48++qjan93LL7+sv/71r0pISFCXLl1qfY4YY7R161YlJSVFtr322mt65ZVXFA6H1apVK02fPl2pqalatWqVZs2aFfncEyZM0MCBAyv8jBYvXqxHH31UcXFxOu200yLHnD9/vt5//30988wzlV7/8MMPmjFjhoqKipSTk6Nu3brpscceU2xsbIU2nDp1qvbt2ydJ6tu3r2655ZZaf85y+fn5iouLU3x8vKSDPYOffvqpbrjhBl111VUaOHCgJOmPf/yjJOnGG2/U/fffr82bNysvL08tWrTQnDlzdOKJJ+ryyy9XUlKS/vOf/2jcuHFavHixxo8fr4yMDD399NOVro3+/fvrnnvu0a5du3TNNdfo+eef1+rVqzVnzhwVFxfL7XbrpptuUr9+/Wr1WbZs2aL4+HjFxMRIqvocrO567N+/v9LS0rRhwwbdeuutSktLO+w5VlZWpgcffFCrV6+Wz+fTscceq5kzZyo2Nvaw2/ft26dhw4bpyy+/VDAY1KxZs/Tpp5/K4/EoLS1N06ZNU0JCgvr3769Ro0bp008/1Y4dOzRixIg6tSsAyACAA2zZssV07979sPuefPJJM2vWLBMOh40xxvzP//yPue+++0w4HDb9+vUza9euNcYYk5mZaW677TZjjDGXX365Wbp0qTHGmJKSEnP55Zebd955x2zZssV06dLFrFy50hhjzNatW83ll19uiouLjTHGvP3222bo0KHGGGPmzJljbr31VhMKhUxBQYEZNmyYmTp1arXH/7nx48eb9957zxhjzIcffmjGjh1rjDHmvPPOM19++aUxxpiPP/7YPPnkkzX+jJYvX2569+5tzjrrLDNx4kTz3HPPmZ07d0b2X3bZZZH3mjp1qvnzn/8c2Vf+urS01Jx99tlm+fLlxhhjFi5caLp06WK2bNlifvjhBzN06FCzd+9eY4wxGzduNL179zZFRUXmiSeeMOedd54pKCgwxhgzYcIE8/jjj1d6ryeeeMI88MADFerOzs42/fr1M59//nm1P7tvv/3W9OrVy+zevdsYY8z06dNNv379Dvuz6NevnxkwYIAZNmyY+fWvf21+/etfm2nTppns7GxjjDErVqwwl156qTlw4EDkZ5yRkWGMMeY3v/mNefvtt40xxqxfv97cf//9FT5HTk6O+eUvf2m+//57Y4wxTz/9tOnSpYsxxpg33njDXHfddZE6Dn09a9Ys8+abbxpjjAkEAmbo0KFm0aJFxhhjunTpYvbs2WOeeuopM336dGOMMUVFReaWW24x+/fvP+xnPNznHT58uBk4cKDp1q2beeSRRyL7y4//+uuvR+opKyszffr0MT/88IN57733zIMPPhj5+unTp5sZM2YYYw6eN9OmTYvsKz+Pqrs2PvvsMzNkyBBjjDF5eXlmwIABZsuWLcYYY3bu3GnOOeccs23btkqf47LLLjP9+vUzw4cPN+eee67p1auXmTx5slm3bp0xxlR7DlZ3Pfbr18889dRTkfep6hxbuXKlycjIiPxdMnv2bPPFF19Uuf3Qv5cef/xxc9NNN5lAIGBCoZC58847I23Zr18/M2vWrMjnP/300yPnIgAcCXruADjehx9+qIKCgshzQMFgUG3btpXL5dJFF12kBQsW6PTTT9f8+fM1ZcoUHThwQCtXrlR+fr4ef/xxSdKBAwf03XffKS0tTV6vV927d5ckHXPMMZo9e7YWLlyozZs3a82aNSoqKpIkffTRR5o2bZrcbrcSEhI0atQobdiwodrjDx48uELtF198sRYsWKCMjAzNnz9fl1xyiSRpyJAhuummm9S3b1/17t1bv/3tb2v8OfTq1UsffvihvvrqK61atUr/+te/9Kc//Ul//etflZaWVquf5caNG+X1etWrVy9J0tChQ3XvvfdKkpYtW6bdu3fryiuvjHy9y+VSdna2JOmss86KDJs75ZRTlJ+fX+P77d27V7/97W9166236swzz6z2Z7dz50717t1bKSkpkqQxY8ZUO8SyfJjili1bdNVVV+nkk0/WcccdJ+ngObN58+YKz0vt379feXl5GjRokGbMmKEPPvhAZ599tm699dYKx/3iiy/UpUuXSM/QmDFj9Mgjj9T4We+44w4tW7ZMzz33nH788Uft3r1bBw4cqPA1v/71r3Xddddpx44dOvvss3XbbbcpMTGxxmMf+nmlg71dV155pU466SQNHTo08jWDBw/W7NmzlZOTo2+//VbHH3985L/jjjtOL730kjZv3qzPP/+8wjOuPXv2rPR+1V0bh/rqq6+Uk5OjG2+8MbLN5XJpw4YNOvrooyt9ffmwzPJzo3379jrllFMkVX8OVnU9/vwzVHeO9enTRx6PR6NHj1afPn00cOBApaWlaf/+/YfdfuizqP/+9781efJk+Xw+SQefHzz0M5933nmSDg7lbNu2rfLz8yPnIwDUFuEOgOOFw2Hddddd6tu3rySpqKhIpaWlkg6Gp1GjRmn06NEqKCjQWWedpcLCQhljNG/ePMXFxUk6GDJiY2O1b98+xcTEyOs9+NfnunXrdMMNN+jKK69U7969deaZZ+qBBx6QJHm9XhljInW43e5IPVUd/+cGDRqkWbNmKSsrSytXrtSsWbMkSZMnT9ZFF12kZcuWaf78+fq///s/vf7661X+DPbs2aMnn3xS06dPV8+ePdWzZ09NnDhRd999t958881K4c7lclWoPRgMRv586Pbyz1n+uXr16hV5pk86+GxXu3bttGTJEvn9/iqPfzjFxcWaOHGiRo0aFQkg1f3s/v73v1c4psfjqfb45Y477jjNnj1bv/nNb/SLX/xCaWlpCofDGjFihO64447I++7evVtJSUkaO3as+vXrp2XLlunjjz/WU089pUWLFlU45qF1lP98Dve5D/253nrrrQqFQho0aJDOPfdc7dixo9LPKC0tTUuXLtWnn36qzz77TKNHj9Zzzz2nV155Rd98840kaezYsTU+u3jcccepf//+WrlyZYVwFxcXp4EDB+rtt9/Wl19+qdGjR0s6ONz11Vdf1fjx4zVs2DC1atWqQnApH955qOqujUOFQiGlpqbqtddei2zbtWuX2rRpU+1naNOmjR577DENHTpUPXr00IABA6o9B6u6Hn/+Gao7x1q0aKG33npLq1ev1meffaZbbrlF11xzjcaPH3/Y7eV/55Qf1+VyVXh9aPsfev3X5voAgMNhtkwAjtenTx9lZmYqEAgoHA5r+vTpkZ6U9u3bKy0tTffee68uvvhiSVJCQoK6d++uv/zlL5IO9tiMGzdOS5curXTslStX6rTTTtNVV12ls846S0uXLlUoFJJ08HmoN954Q+FwWMXFxXr77bflcrmO6PixsbEaMmSI7rzzTg0YMEBxcXEqKytT//79VVxcrHHjxum+++7Thg0bFAgEqvwZJCUlafny5XrxxRcj/2gsLi5WdnZ2pNfD4/GorKxMktS6detIWNi1a5c+//xzSQefzTLG6KOPPpIkLV26NNID16tXLy1btkxZWVmSDvZcDh8+XCUlJdW2z6HvWy4UCumWW25Rt27dNGHChMj26n52vXv31rJly7Rz505J0oIFC6p930OdccYZGjlypO6//36Fw2H16dNH77zzjnbv3i3p4CyeV1xxhaSD4Wn9+vW68MIL9eCDD2r//v3KycmJHOvMM8/Upk2b9N1330k6+FxduTZt2uj7779XaWmpgsGg3n///ci+Tz75RDfeeGOk93bNmjWRc6ncnDlz9L//+786//zzdffdd6tz5876/vvv9fDDD+utt97SW2+9VatJacp7pw7XY3vJJZdowYIFWr16deTZu08++STyS5ATTjhBH3zwQaXafq66a8Pj8USCTffu3bV582atXLlSkrR+/XoNHDhQu3btqvFzHHfccZo4caIefvhhHThwoNpzsKrr8eeqO8f+9a9/6corr1SPHj00adIkjRw5Ut98802V2w/161//Wq+88oqCwaDC4bAyMzPVu3fvGj8jABwJeu4AOMaBAwcqLYcwb9483XDDDfrDH/6gUaNGKRQK6eSTT9add94Z+ZrRo0fr5ptv1ty5cyPb5syZowcffFDDhg1TIBDQ0KFDNXz48EpT/g8dOlSLFy/WoEGDFA6H1a9fP+Xn56uwsFATJkzQjBkzNGzYMCUmJqpt27aR3quqjn84o0eP1t/+9jfdf//9kg72BN111126/fbb5fV65XK59Pvf/14xMTFaunSp5s2bp+eee67CMbxer55//nn98Y9/1EsvvaT4+Hi5XC6NGjUqEmr79++vRx55RMFgUJdffrluv/12DRw4UMcee6x+9atfSZJ8Pp/+9Kc/6f7779cjjzyik08+WW3btpUkde7cWTNmzNCtt94qY4y8Xq/mzp2rFi1aVNtu55xzTqRHstx7772nDz/8UKeddppGjhwZCaTPPvtstT+7O+64Q1dccYVatGhR66Gm5W699VYNGjRIr776qsaOHavf/va3uvrqqyOB/KmnnpLL5dLtt9+u3//+93rsscfkcrl000036dhjj40cp02bNpozZ45uv/12+Xy+ChPdlPdgDRo0SCkpKUpPT48MDZw8ebJuvPFGxcfHKyEhQWeeeWZkSGu5K664QnfeeaeGDh2qmJgYde3aVUOGDKnV57v99tvl9/vlcrlUXFysQYMG6aKLLqr0daeddpo8Ho8yMjIivUlXX3217r333kjvcPfu3bVx48Zq36+6a6Nz586KjY3VxRdfrNdee01PPPGEZs+erdLSUhljNHv27Ao/0+pcc801evPNNzV37lzddtttVZ6D1V2PP1fVORYKhfTvf/9bQ4cOVXx8vJKSkvTggw+qQ4cOh91+qOuvv15/+MMfNHLkSJWVlSktLU3Tp0+v1WcEgNpyGfr9AaBRvPPOO0pISFDfvn0VDoc1adIk9e7dW5deeqnVpQHNDtcjgOaAcAcAjWTjxo269957VVxcrGAwqPT0dN11112RCRUARA/XI4DmgHAHAAAAAA7AhCoAAAAA4ACEOwAAAABwAMIdAAAAADgA4Q4AAAAAHMB269zt21ekcNj5c8C0bZugPXsKrS4DNaCd7IF2avpoI3ugneyBdmr6aCN7aIrt5Ha71Lp11evH2i7chcOmWYQ7Sc3mc9od7WQPtFPTRxvZA+1kD7RT00cb2YPd2olhmQAAAADgAIQ7AAAAAHAAwh0AAAAAOADhDgAAAAAcgHAHAAAAAA5AuAMAAAAAByDcAQAAAIADEO4AAAAAwAEIdwAAAADgAIQ7AAAAAHAAwh0AAAAAOADhDgAAAAAcgHAHAAAAAA5AuAMAAAAAByDcAQAAAIADEO4AAAAAwAEIdwAAAADgAIQ7AAAAAHAAS8Ld448/rsGDB2vIkCH6y1/+YkUJAAAAAOAo3mi/4eeff67PPvtM//jHP1RWVqbBgwerb9++OvHEE6NdCgAAAAA4RtR77s466yy9+OKL8nq92rNnj0KhkOLj46NdBgAAAAA4issYY6x44yeeeEL/93//p4yMDM2cOVMul8uKMgAAFvvd4q8kSU8M6G5pHQAA2J1l4U6SiouLNXHiRA0ePFhjxoyp1ffs2VOocNiykqMmJSVROTkFVpeBGtBO9kA7NW0zVmfJ5ZKm90i1uhTUgGvJHminpo82soem2E5ut0tt2yZUvT+KtUiSsrKytH79eklSXFycBgwYoA0bNkS7DAAAAABwlKiHu61bt+qee+5RIBBQIBDQ0qVL9ctf/jLaZQAAAACAo0R9tsy+fftq7dq1GjlypDwejwYMGKAhQ4ZEuwwAAAAAcJSohztJmjRpkiZNmmTFWwMAmpiTkuIVG2vJ7QgAAEfhbgoAsNS41A5N8qF1AADshnAHALBUYbBM/kCZ1WUAAGB7hDsAgKUe+XozSyEAANAAoj5bJgAAAACg4RHuAAAAAMABCHcAAAAA4ACEOwAAAABwACZUAQBY6tTWCfL7fVaXAQCA7RHuAACWuuiE9qxzBwBAAyDcAQAslVMcUFlhCTckAADqiXspAMBSc9dvYZ07AAAaABOqAAAAAIADEO4AAAAAwAEIdwAAAADgAIQ7AAAAAHAAJlQBAFjqjLaJiouLsboMAABsj3AHALDU0E7tWOcOAIAGQLgDAFgqu6BYBV6XEq0uBAAAm+OZOwCApV74frseW7nJ6jIAALA9wh0AAAAAOADhDgAAAAAcgHAHAAAAAA5AuAMAAAAAB2C2TACApX7VLkkt4lnnDgCA+iLcAQAsNeDYZNa5AwCgARDuAACW2pBXpJ0mrKNcPCkAAEB9cCcFAFjq7//Zqee++sHqMgAAsD3CHQAAAAA4AOEOAAAAAByAcAcAAAAADkC4AwAAAAAHYLZMAICl+h7VWi0SYq0uAwAA2yPcAQAs1ffoNqxzBwBAAyDcAQAstWZPgVoGgzrB57O6FAAAbI1n7gAAlnpr82797Ztsq8sAAMD2CHcAAAAA4ACEOwAAAABwAMIdAAAAADgA4Q4AAAAAHIDZMgEAlhp4TFsltPRbXQYAALZHuAMAWCq9fSvWuQMAoAEQ7gAAllqxK08JxSU6NY7eOwAA6oNn7gAAlnp/2x7N/26b1WUAAGB7hDsAAAAAcADCHQAAAAA4AOEOAAAAAByAcAcAAAAADsBsmQAAS43o1E4tWecOAIB6o+cOAGCpX7RN1FlHt7G6DAAAbI+eOwCApT7avlct9hepZ8sWVpcCAICt0XMHALDURzv36b2snVaXAQCA7dFzhwa1NitXi1ZkKze/RMlJfmWkd1RaarLVZQEAAACOR7hDg1mblavMJRvl8bgV7/cqryigzCUbJYmABwAAADQyhmWiwSxakS2Px61Yn0cul0uxPo88HrcWrci2ujQAAADA8Qh3aDC5+SWK8VY8pWK8buXml1hUEQAAANB8MCwTDSY5ya+8ooBifZ7ItkBZWMlJrF8FoGpjTjxKSUlxVpcBAIDt0XOHBpOR3lGhUFilwZCMMSoNhhQKhZWR3tHq0gA0YV1btdDp7ZKsLgMAANuj5w4NpnzSFGbLBHAkFm/NVYu9+9W7TUurSwEAwNYId2hQaanJhDkAR+Sz3flyuUS4AwCgnhiWCQAAAAAOQLgDAAAAAAcg3AEAAACAAxDuAAAAAMABmFAFAGCpK086Wq1bx0tlxupSAACwNXruAACW6pgYpxNbJ1hdBgAAtkfPHQDAUm9v3q243Xk6r10rq0sBAMDW6LkDAFhq9Z4CLd+2x+oyAACwPcIdAAAAADgA4Q4AAAAAHIBwBwAAAAAOQLgDAAAAAAdgtkwAgKWuP/k4tW7TQioOWl0KAAC2Rs8dAMBSKXEx6pDgt7oMAABsz5Keu6eeekrvvfeeJKlv376aMmWKFWUAAJqAN37YJf+OvRrSoY3VpQAAYGtR77lbvny5PvnkEy1YsEBvvvmm1q1bpyVLlkS7DABAE7FuX6FW79xndRkAANhe1HvuUlJSdOeddyomJkaSlJqaqu3bt0e7DAAAAABwlKiHu5NOOiny5x9//FHvvfeeXnnllWiX0aCe+25rpW2nt0nQr9q1UiAU1l+/rxxez0huqV8mt1RRMKSXs3ZU2n9BsL2O9/mUVxrUaz/sqrS/z1GtdHKrBOUUB/Tm5t2V9vfr0Eadk+K1/UCp3snOqbR/wDFt1SkxTpsLirV4255K+4d0TNHR8bHalH9A/9qxt9L+kZ3aKSUuRuvzCvXJzrxK+0ef0F6tYn1au6dAK3LyK+2/NLWDWvg8+iJ3v1bn7q+0/4qTjlaMx63Pdufp672Flfb/ttuxkqSPd+zTd/lFFfb53C5d2eUYSdIH2/coa39xhf3xXrfGdz5akvT+llxlF5VU2J/k8+qS1KMkSW9n52jHgdIK+5P9Po06vr0k6cWvN2tr3oEK+zvEx2poxxRJ0qtZO5UfLKuwv2MLvwYelyxJyty0XQfKwhX2p7aMU/+j20qSXti4TcGwqbC/W1IL/bpDa0mNc+6lpyQprW2io849X5ZHwWBIknPOvQU/7lJuScUJSOx67pWGwnJJWrunwHHn3qE495reuVfOaX/vHfp3nsS51xTPvfI2ctq593N2P/euS0msVFNTZ9lsmd9//70mTJigKVOm6Pjjj6/197Vtm9B4RdWRL8tTaVtCgl8pKYkqDYXl+7Hy/sTEg/v9gTL5sivvl6SUlER5igPybc2ttC+pZZxSUhIVKiyRb3vl709qFaeU5EQV7/fIt6Py/lat45XSOkH5Xpd8u/Mq7W/dOl4pLeOV4zLy5Vb+/jZtWiglwa/t4ZB8ewoq7W/bNkFt4mLUMhiUL6/yxdo2OUGJMV4llgbk+9nFKknJKYmK9biVcKBEvoLiSvtT/nuxtSg4IN+BihdrjMf90/68QvmKAxX2x/q8kf3xe/bLF6h4o4j1/7Q/LidPvp/dKPx+X2S/du6Vz1fx5xMX99P+2G258qnijSI+Puan/dk+BV0Vj98iPjayP+ZHrxSqeCNqkfDT/sY491q29Dvy3CtvJ6ece/6de+X72blh13PP9d//O/Xci+x3yrnn9znm3CvnxHPv0HuTY849B/29Jx1sIyeeexX22/zcO7QGu3AZY0zNX9awvvjiC/3ud7/TXXfdpSFDhhzR9+7ZU6hwOOolR11KSqJycipfRGhaaCd7oJ2athmrs+RySdN7pFpdCmrAtWQPtFPTRxvZQ1NsJ7fbVW1nV9R77nbs2KEbb7xRjz76qHr16hXttwcANDG3nt5JycmJKsmv/FtbAABQe1EPd88//7xKS0s1a9asyLaxY8dq3Lhx0S4FANAEJPi8SozxqqTmLwUAANWIeri75557dM8990T7bQEATdQrWTsUuzVHFx6bYnUpAADYWtTXuQMA4FDf5x/QupzKs6gBAIAjQ7gDAAAAAAcg3AEAAACAA1i2zh3qZm1WrhatyFZufomSk/zKSO+otNRkq8sCAAAAYDHCnY2szcpV5pKN8njcivd7lVcUUOaSjZJEwAMAAACaOcKdjSxakS2Px61Yn0eSFOvzqPS/2wl3AOzq3jNSm+RCsQAA2A3P3NlIbn6JYrwVmyzG61ZuPqtDAQAAAM0d4c5GkpP8CpSFK2wLlIWVnOS3qCIAqL+/btymJ1ZusroMAABsj3BnIxnpHRUKhVUaDMkYo9JgSKFQWBnpHa0uDQDqbHNhiTbtK7S6DAAAbI9n7myk/Lk6ZssEAAAA8HOEO5tJS00mzAEAAACohGGZAAAAAOAA9NwBACzldklul8vqMgAAsD3CHQDAUvf0YJ07AAAaAsMyAQAAAMAB6LkDAFjqz99tVUyWR79J7WB1KQAA2BrhDgBgqe0HSsUjdwAA1B/DMgEAAADAAQh3AAAAAOAADMu0ubVZuVq0Ilu5+SVKTvIrI70ji5wDAAAAzRDhzsbWZuUqc8lGeTxuxfu9yisKKHPJRkki4AGwDZ/bJY+bgSQAANQX4c7GFq3IlsfjVqzPI0mK9XlU+t/thDsAdjGt+4mscwcAQAMg3DVRq9bv0t8Xf1ftcMvc/BLF+ys2YYzXrdz8kmiWCgAAAKAJINw1QWuzcjVv6SbJpWqHWyYn+ZVXFIj03ElSoCys5CR/1GsGgLqa++0W+XxuXXvSMVaXAgCArfGQQxO0aEW2vF6XYn0euVwH/+/xuLVoRXaFr8tI76hQKKzSYEjGGJUGQwqFwspI72hR5QBw5HJKAtpRyIgDAADqi567Jig3v0RJCTEqC5nItsMNtyzvxWO2TAAAAACEuyYoOcmvwpJghdnjqhpumZaaTJgDAAAAwLDMpigjvaPKygzDLQEAAADUGj13TVBaarKSkuJrnC0TAJwgzuOW28PvGgEAqC/CXRPV8+T26pQcb3UZANDo7vjFCaxzBwBAA+BXpQAAAADgAPTcAQAs9eS6zfJ6PLq+27FWlwIAgK0R7gAAltpXWiaXq8zqMgAAsD2GZQIAAACAAxDuAAAAAMABCHcAAAAA4AA8cwcAsFSizyMP69wBAFBvhDsAgKUmn34869wBANAA+FUpAAAAADgAPXcAAEs9+vWP8njc+t0pHa0uBQAAWyPcAQAsVRAMyVUWsroMAABsj2GZAAAAAOAAhDsAAAAAcADCHQAAAAA4AM/cAQAs1TrWK6/HY3UZAADYHuEOAGCpSad2Yp07AAAaAMMyAQAAAMAB6LkDAFjqj2t+kNvj1m2ndbK6FAAAbI1wBwCwVHEoLFc4bHUZAADYHsMyAQAAAMABCHcAAAAA4ACEOwAAAABwAJ65AwBYKsUfI5+P3zUCAFBfhDsAgKWuP+U41rkDAKAB8KtSAAAAAHAAeu4AAJaa+dV/5HG7NSXteKtLAQDA1gh3AABLBcNGZSZkdRkAANgewzIBAAAAwAHouQPqYdX6Xfr74u+Um1+i5CS/MtI7Ki012eqyAAAA0AwR7oA6WpuVq3lLN0kuKd7vVV5RQJlLNkoSAQ8AAABRx7BMoI4WrciW1+tSrM8jl+vg/z0etxatyLa6NMBWjo6PVaeW8VaXAQCA7dFzB9RRbn6JkhJiVBYykW0xXrdy80ssrAqwn2u7Hcs6dwAANAB67oA6Sk7yqzRYcYa/QFlYyUl+iyoCAABAc0a4A+ooI72jysqMSoMhGXPw/6FQWBnpHa0uDbCVh77M0uQla6wuAwAA22NYJlBHaanJSkqKZ7ZMoJ7CRjIyNX8hAACoFuEOqIeeJ7dXp2QmggAAAID1GJYJAAAAAA5AuAMAAAAAB2BYJgDAUp0S/IqJ4XYEAEB9cTcFAFjqii7HsM4dAAANgGGZAAAAAOAA9NzBUdZm5WrRimyWJgBsZMbqLLlc0vQeqVaXAgCArRHumjknhaG1WbnKXLJRHo9b8X6v8ooCylyyUZJs+5kAAACA2mJYZjNWHobyigIVwtDarFyrS6uTRSuy5fG4FevzyOVyKdbnkcfj1qIV2VaXBgAAADQ6y8JdYWGhhg4dqq1bt1pVQrPntDCUm1+iGG/FUzrG61ZufolFFQEAAADRY0m4W7NmjcaNG6cff/zRirfHfzktDCUn+RUoC1fYFigLKznJb1FFAAAAQPRYEu5effVV3XfffWrXrp0Vb4//cloYykjvqFAorNJgSMYYlQZDCoXCykjvaHVpAKpxUlK8Tk1paXUZAADYnssYY6x68/79++vFF1/Usccea1UJzdqq9bv0zPy18noPDsksDYZUVmY04cI09Ty5vdXl1cmq9bs0/8NN2r33gNq1ideF53a27WcBAAAAjoTtZsvcs6dQ4bBleTRqorGgb6fkeI09r3Ol2TI7JcfbdjHhTsnxmnxxWoVtjflZWHjZHminpq0wWKbk5ESV5BdbXQpqwLVkD7RT00cb2UNTbCe326W2bROq3G+7cIeGlZaazDIBACz1yNebWecOAIAGwFIIAAAAAOAAhDsAAAAAcABLh2V+8MEHVr49AAAAADgGPXcAAAAA4ABMqAIAsNSprRPk9/usLgMAANsj3AEALHXRCe2b5HTTAADYDeEOAGCpnOKAygpLuCEBAFBP3EsBAJaau34L69wBANAAmFAFAAAAAByAcAcAAAAADkC4AwAAAAAHINwBAAAAgAMwoQoAwFJntE1UXFyM1WUAAGB7hDtUsjYrV4tWZCs3v0TJSX5lpHdUWmqy1WUBcKihndqxzh0AAA2AcNeM1Ca0rc3KVeaSjfJ43Ir3e5VXFFDmko2SRMAD0CiyC4pV4HUp0epCAACwOZ65aybKQ1teUaBCaFublVvh6xatyJbH41aszyOXy6VYn0cej1uLVmRbVDkAp3vh++16bOUmq8sAAMD2CHfNRG1DW25+iWK8FU+LGK9bufkl0SwXAAAAwBEi3DUTtQ1tyUl+BcrCFbYFysJKTvI3eo0AAAAA6o5w10zUNrRlpHdUKBRWaTAkY4xKgyGFQmFlpHeMZrkAAAAAjhDhrpmobWhLS03W+Au6qFWLGB0oKVOrFjEaf0EXJlMBAAAAmjhmy2wmysNZbZY4SEtNJswBiJpftUtSi3jWuQMAoL6OKNwFAgHl5ubq6KOPbqx60IgIbQCaogHHJrPOHQAADaDGcLdkyRJ99tlnmjx5soYPH66CggLddNNNuuKKK6JRX7PHguIAnG5DXpF2mrCOcvGkAAAA9VHjnfSZZ57RJZdcosWLF6t79+7617/+pbfeeisatTV7tV2bDgDs7O//2annvvrB6jIAALC9GsOdMUZdu3bV8uXLdc455yghIUHGmGjU1uyxoDgAAACA2qpxWKbb7da7776rTz75RFOnTtVHH30kl8sVjdqavdz8EsX7KzZRc15QnCGqAAAAQNVq7LmbOnWqXn31VU2ePFkpKSmaO3eu7r777mjU1uyxoPhPGKIKAAAAVK/GnruePXvqhRdeiLyeN29eY9aDQ2Skd1Tmko0q1cEeu0BZuNkuKH7oEFVJivV5VPrf7fTeAQAAANWEu2HDhlX7jQsXLmzwYlDRkaxN53QMUQWcq+9RrdUiIdbqMgAAsL0qw9306dOjWQeqwNp0ByUn+ZVXFIj03EnNd4gq4DR9j27DOncAADSAKsPdWWedFflzXl6eiouLZYxRKBRSdjazNSK6GKIKONeaPQVqGQzqBJ/P6lIAALC1Gp+5e/zxx/Xss89Kkjwej4LBoDp37sywTEQVQ1QB53pr8265XNL0HqlWlwIAgK3VGO7eeust/etf/9KsWbM0ZcoUffbZZ/roo4+iURtQAUNUAQAAgKrVuBRCmzZt1K5dO5144on67rvvNHLkSG3cuDEatQEAAAAAaqnGnjuv16vs7GydeOKJWrVqlfr06aPS0tJo1AYbYYFxAAAAwFo19txNmDBB06dP17nnnqvFixfr3HPPVXp6ejRqg02wwDgAAABgvRp77vr166d+/fpJOvj83ebNm9WtW7dGLwz2wQLjAOpj4DFtldCSZU0AAKivGsPdX/7yl0rbPv30U1111VWNUhDshwXGAdRHevtWrHMHAEADqDHcHTp5SiAQ0MqVK9WrV69GLQr2Eu0Fxnm+D3CWFbvylFBcolPj6L0DAKA+agx3M2fOrPB6165duvvuuxutINhPNBcYL3++z+NxV3i+TxIBD7Cp97ftkWu7dCrr3AEAUC81Tqjyc+3bt9e2bdsaoxbYVFpqssZf0EWtWsToQEmZWrWI0fgLujRK2Dr0+T6Xy6VYn0cej1uLVmQ3+HsBAAAAdnJEz9wZY/TNN9+obdu2jVoU7CdaC4zzfB8AAABweEf0zJ0kdejQQVOmTGm0goDqRPv5PgAAAMAujviZO8BK0Xy+DwAAALCTKsPd5ZdfLpfLVeU3vvjii41SEFCd8qGfzJYJOMeITu3UknXuAACotyrD3WWXXSZJWrJkiQoLC3XRRRfJ4/HorbfeUsuWLaNWIPBz0Xq+D0B0/KJtIuvcAQDQAKoMdwMHDpQkPf/885o3b57c7oMTa5577rkaM2ZMdKoDADjeR9v3qsX+IvVs2cLqUgAAsLUal0LYt2+fSktLI6+LioqUn5/fqEUBAJqPj3bu03tZO60uAwAA26txQpWhQ4fqkksu0QUXXCBjjBYtWqRLLrkkGrUBAAAAAGqpxnB3880367TTTtOnn34qSbrzzjvVt2/fRi8MjWttVi6TkgAAAAAOUmW4y8rKUmpqqtatW6ejjjpKo0aNiuxbt26dTj311KgU2NyUh669hQG1SYhplNC1NitXmUs2yuNxK97vVV5RQJlLDq5nSMADAAAA7KnKcDd79mw988wzmjRpUqV9LpdLS5cubdTCmqNDQ1diXOOFrkUrsuXxuCMLgcf6PCr973bCHeqDHmEAAADrVBnunnnmGUnSBx98ELVimrtDQ5fL5Wq00JWbX6J4f8Wmj/G6lZtf0mDvgeaHHmHU1ZgTj1JSUpzVZQAAYHs1zpaZlZWl1157TcYYTZ48Weeff74+++yzaNTW7OTmlyjGW7FJGiN0JSf5FSgLV9gWKAsrOYlFhFF3h/vlhMfj1qIV2VaXhiaua6sWOr1dktVlAABgezWGu/vuu0+xsbH68MMPtXPnTj388MN69NFHo1FbsxOt0JWR3lGhUFilwZCMMSoNhhQKhZWR3rFB3wfNS7R+OQHnWbw1V29u2GZ1GQCAZmZtVq5mv7xaU+Yu1+yXV2ttVq7VJdVbjeGutLRUw4cP1yeffKJBgwYpPT1dwWAwGrU1O9EKXWmpyRp/QRe1ahGjAyVlatUiRuMv6MLQOdQLPcKoq8925+uDzTlWlwEAaEbKHyfJKwpUeJzE7gGvxqUQAoGAcnNz9eGHH+qZZ55Rbm5uhUXN0XDKw9WiFdnaVxhQ60aaLbP8vQhzaEgZ6R2VuWSjSnWwxy5QFqZHGAAANElOnWCwxnA3ZswY9evXT4MGDVLnzp117rnn6oYbbohGbc1SeehKSUlUTk6B1eUAtXboLyeYLRMAADRlTp1gsMZwd+mll2rs2LFyuw+O4FywYIFat27d6IWhfpiSHlagRxgAANhBcpJfeUWBSM+d5IzHSWp85q6oqEgPPfSQrrjiCuXl5enRRx9VUVFRNGpDHTl1DDEAAADQEJw6wWCN4e6hhx5SYmKi9uzZo9jYWBUWFuree++NRm2oI6akB2AnV550tG45s7PVZQAAmhGnTjBY47DM9evXa+bMmfroo48UFxenOXPmaOjQodGoDXXk1DHEAJypY2KcUlon8JwxACCqnPg4SY09d+XP2pULhUKVtqFpYUp6AHby9ubdmrdui9VlAABgezWmtDPPPFN//OMfVVJSoo8//liTJk3SWWedFY3aUEdOHUMMwJlW7ynQ8m17rC4DAADbqzHc3X777YqPj1diYqIeffRRde3aVVOnTo1Gbagjp44hBgAAAFC1Gp+58/l8uvHGG3XjjTdGtm3atEmdO/Pwe1PmxDHEAAAAAKpWZc/d1q1bddttt+nBBx9UcXGxpIPLIsycOVMjR46MVn0AAAAAgFqoMtzdddddat26tXJycvTMM89o7dq1GjJkiJYtW6Y///nP0awRAAAAAFCDKodl7ty5Uy+++KJKSkp04YUX6tVXX9WVV16pq6++Wl5vjaM5AQColetPPk6t27SQioNWlwIAgK1VmdLi4+MlSX6/X/n5+Zo9e7b69OkTtcIAAM1DSlyMUhL8yiHcAQBQL7XqgmvTpg3BDgDQKN74YZf8O/ZqSIc2VpcCAICtVfnMncvlivyZYZgAgMaybl+hVu/cZ3UZAADYXpWpbcOGDTrjjDMkSSUlJZE/G2Pkcrm0evXq6FQIAAAAAKhRleFuyZIl0awDAAAAAFAPVYa7Y445Jpp1ALa0av0u/X3xd8rNL1Fykl8Z6R1ZPB4AAACW4GE6oI7WZuVq3tJNkkuK93uVVxRQ5pKNkkTAAwAAQNRVOaFKY1q4cKEGDx6sAQMGKDMz04oSgHpbtCJbXq9LsT6PXK6D//d43Fq0Itvq0gBbufX0Tnr43NOsLgMAANuLes/drl279Oijj2r+/PmKiYnR2LFjlZ6ers6dO0e7FKBecvNLlJQQo7KQiWyL8bqVm19iYVWA/ST4vEqM8YorBwCA+qky3E2bNq3ab5w5c2ad3nD58uX61a9+pVatWkmSBg4cqEWLFummm26q0/EAqyQn+VVYEpTH/VMHeKAsrOQkv4VVAfbzStYOxW7N0YXHplhdCgAAtlblsMyTTjpJJ510kgoKCrRhwwZ17dpVp5xyin788UeFQqE6v+Hu3buVkvLTDbxdu3batWtXnY8HWCUjvaPKyoxKgyEZc/D/oVBYGekdrS4NsJXv8w9oXc5+q8sAAMD2quy5u/rqqyUdXBIhMzNTcXFxkqRLLrlEv/nNb+r8huFwuMIC6eXr5tVW27YJdX5vO1m1fpfmf7hJu/YeUPs28brw3M7qeXJ7q8vCIc5LSVRSUrzmf7hJu/ceUDuL2olzpXZSUhKtLgFVKL8F0Eb2QDvZA+3U9NFG9mC3dqrxmbs9e/YoJiYm8trlcmnfvn11fsOjjjpKq1atirzOyclRu3btav39e/YUKhw2NX+hjR06C6Pf51bOvgP639e/0vgLujALYxPT8+T26pQcX2FbTk5B1N5/bVauMpdslMfj5lypRkpKYlTbBUfGmIMBjzZq+riW7IF2avpoI3toiu3kdruq7eyqcbbMXr166dprr9Ubb7yh119/Xddcc4369+9f54LOPvtsffrpp9q7d6+Ki4u1ePFinXPOOXU+nhMxCyNqa9GKbHk8bs4VAAAA1NxzN336dGVmZmrJkiWSpEGDBmns2LF1fsP27dtr8uTJ+s1vfqNgMKiLL75YaWlpdT6eEzELI2orN79E8f6KlzHnCgAAQPNUY7jzer0aOHCgjj/+ePXu3Vu7d++W212/5fGGDRumYcOG1esYTsYsjKit5CS/8ooCivV5Its4V2A3956R2iSHvgAAYDc1prQPP/xQY8eO1QMPPKC9e/dqyJAh+uc//xmN2potZmFEbWWkd1QoFOZcAQAAQM3h7k9/+pNeffVVtWzZUu3atdPLL7+sJ554Ihq1NVtpqcmacGGaWrWI0YGSMrVqEcMEGTistNRkjb+gC+cKbO2vG7fpiZWbrC4DAADbq3FYZigUqjCb5cknn3xESxegbg43CyNwOGmpyYQ52NrmwpKDyyEczxIeAADUR409d3Fxcdq+fXsk0K1atUqxsbGNXhgAAAAAoPZq7Lm77bbbdPXVVysnJ0djxozRjz/+qCeffDIatQEAAAAAaqnGcHfGGWfo1Vdf1ZdffqlwOKxf/OIXatOmTTRqAwAAAADUUo3DMqdOnaqysjL17dtX/fr1U5s2bTRhwoRo1AYAaAbcLsnDs9wAANRbjeFu8eLFGjdunH744YfItl27djVqUQCA5uOeHql69IJfWF0GAAC2V2O469Spk+68805deeWVWrVqVTRqAgAAAAAcoRqfuXO5XOrXr59at26t3/3ud5o6dapiYmKiURuO0NqsXC1aka3c/BIlJ/mVkd6RKfIBNHl//m6rYrI8+k1qB6tLAQDA1moMd8YYSVL37t3117/+Vdddd5327t3b6IXhyKzNylXmko3yeNyK93uVVxRQ5pKNkhQJeIQ/AE3R9gOl4pE7AADqr8ZhmTfddFPkzyeccIJeeeUVDRkypFGLwpFbtCJbHo9bsT6PXC6XYn0eeTxuLVqRLemn8JdXFKgQ/tZm5VpcOQAAAICGUGXP3aeffqpevXopHA5r8eLFFfb16dOn0QvDkcnNL1G8v2Jzxnjdys0vkVQx/ElSrM+j0v9up/cOAAAAsL8qw90777yjXr166aWXXqq0z+VyacCAAY1aGI5McpJfeUWBSHiTpEBZWMlJfkk1hz8AAAAA9lZluHvooYckSXfccYfS0tIq7Fu+fHnjVoUjlpHeUZlLNqpUB0NboCysUCisjPSOkmoOfwBgFZ/bJY+7xqcEAABADaoMd99++62MMZo6dar+53/+JzKxSllZme6///5KQzVhrfKhlVVNmFJT+KsJk7EAaCzTup+olJRE5eQUWF0KAAC2VmW4e+WVV7Rs2TLt3r27wqQqXq9XF1xwQVSKw5FJS02uMnDVFP6qU5uZOAEAAABYq8pw9+CDD0qSHn30UU2ePDlqBaHxVBf+qsNkLAAa09xvt8jnc+vak46xuhQAAGytxnXubrnlFn3++efKz8+PDM2UxIQqzQiTsQBoTDklAblKra4CAAD7qzHc3Xvvvfroo4/UqVOnyDZmy2xemIwFAAAAaPpqDHfLli3Tu+++q4SEhGjUgyaovpOxAAAAAGh8NYa7o48+mmDXzNVnMhYAAAAA0VFjuDvjjDM0efJk9evXT37/T8PwGJbZvNR1MhYAqEmcxy23h3XuAACorxrD3ZdffilJeu211yLbeOYOANBQ7vjFCaxzBwBAA6gx3L300kvRqAMAomJtVi5DjAEAgCPVOA4mJydH1113nQYOHKg9e/bommuuUU5OTjRqA4AGtTYrV5lLNiqvKKB4v1d5RQFlLtmotVm5VpfWrD25brNmfLze6jIAALC9GsPdAw88oPPPP1+xsbFq2bKlunXrprvvvjsatQFAg1q0Ilsej1uxPo9cLpdifR55PG4tWpFtdWnN2r7SMuUWs9AdAAD1VWO427Ztmy655BK53W75fD7dcccd2rFjRzRqwxFam5Wr2S+v1pS5yzX75dX0RgA/k5tfohhvxb/2Yrxu5eaXWFQRAABAw6nxmTuXy6VwOBx5XVhYWOE1moby4WYej7vCcDNJtn6eiOej0JCSk/zKKwoo1ueJbAuUhZWc5K/muwAAAOyhxp67AQMG6Pbbb1dBQYHmzZunK664QoMGDYpGbTgCThxuxvNRaGgZ6R0VCoVVGgzJGKPSYEihUFgZ6R2tLg0AAKDeauy5mzhxot58802Fw2EtX75cY8aM0ejRo6NRG45Abn6J4v0Vm9Puw80ODaySFOvzqPS/2+m9Q12Unzf0Bjctif/9ZRQAAKifGsOdJI0cOVIjR45s5FJQH04cbubEwArrpaUmE+aamMmnH886dwAANIAqw13//v3lcrmq/MalS5c2SkGom4z0jspcslGlOhiAAmVh2w83c2JgBQAAABpLleHuiSeekCS9/PLL8vl8GjNmjDwej+bPn69gMBi1AlE7Thxu5sTACqCyR7/+UR6PW787hWsbAID6qDLcnXbaaZKk77//Xq+99lpk+7Rp03TxxRc3fmU4Yk4bbubEwAqgsoJgSK6ykNVlAABgezU+c7d//37t3btXbdq0kSTt2rVLhYWFjV4YIDkvsAIAAACNpcZwd8UVV2jYsGHq06ePjDFatmyZ7rjjjmjUBgAAAACopRrD3aWXXqozzjhDn376qSTp2muvVZcuXRq9MAAAAABA7dUY7tatWydJOuussyRJwWBQ69at06mnntq4lQEAmoXWsV55PZ6avxAAAFSrxnA3adKkyJ+DwaBycnJ02mmn6fXXX2/UwgAAzcOkUzuxzh0AAA2gxnD3wQcfVHi9YsUKLVy4sNEKAgAAAAAcOfeRfkN6enpkqCYAAPX1xzU/6M5/fWN1GQAA2F6tn7mTJGOMvvnmG5WUlDRqUQCA5qM4FJYrHLa6DAAAbO+InrlzuVxq27at7r///sasCQAAAABwhGoMdy+//LKOOuqoCts2bdrUaAUBAAAAAI5clc/c5eXlKS8vT9ddd53y8/OVl5en/Px85ebm6qabbopmjQAAAACAGlTZc3fbbbdp2bJlkg5OohL5Bq9XAwcObPzKAADNQoo/Rj7fEc/vBQAAfqbKcPf8889LkqZNm6aZM2dGrSAAQPNy/SnHNZl17tZm5WrRimzl5pcoOcmvjPSOSktNtrosAABqpdpflRpjNGPGDElSYWGhFi9erM2bN0elMAAAomltVq4yl2xUXlFA8X6v8ooCylyyUWuzcq0uDQCAWqmy527Tpk267rrrNH36dPXq1UujR4+WdDDkzZo1S717945akQAA55r51X/kcbs1Je34Wn19Y/WuLVqRLY/HrVifR5IU6/Oo9L/b6b0DANhBlT13s2fP1i233KJ+/frpnXfekSS98847evXVV/Xkk09GrUAAgLMFw0aloVCtvrYxe9dy80sU4614W4zxupWbz9quAAB7qLLnbseOHRo+fLgkacWKFTrvvPPkdrvVoUMHFRYWRq1AoKk4XG/BeSmJVpcFNCuN2buWnORXXlEgcmxJCpSFlZzkr9dxAQCIlip77tzun3Z9+eWXOvPMMyOvS0tLG7cqoImpqrdg1fpdVpcGNCuN2buWkd5RoVBYpcGQjDEqDYYUCoWVkd6x3scGACAaqgx3SUlJ+u6777Rq1Srl5OREwt3q1avVvn37qBUINAWH9ha4XC7F+jzyeNya/+Emq0sDmpXkJL8CZeEK2xqqdy0tNVnjL+iiVi1idKCkTK1axGj8BV143g4AYBtVDsu89dZbdeWVV6qwsFC333674uPj9fzzz+vpp5/Wn/70p2jWCFguN79E8f6Kl0uM163dew9YVBHgHEfHxyrmkKGQ1clI76jMJRtVqoPXYKAs3KC9a2mpyYQ5AIBtVRnuunfvrn//+98qKSlRy5YtJUk9evTQa6+9puOPPz5a9QFNQlXP4rRrE29hVYAzXNvt2Fqvc1cevFiLDgCAyqoMd5IUExOjmJiYyOszzjij0QsCmqKqegsuPLfzER+LRZKB+qF3DQCAw6s23AE4qKregp4nt69Vb0O58olZPB53hYlZDn0PoLl56MssuV0u3dX9RKtLAQDA1gh3QC01RG8BiyQDlYWNZGSsLgMAANurcrZMAA2PRZIBAADQWOi5Q5Pi9OfRWCQZAGB3Tr9XA3ZGzx2ajKoWCl+blWt1aQ2GRZIBAHbWHO7VgJ0R7tBkVLVQ+KIV2VaX1mBYJBmorFOCX51bJ1hdBoBaaA73asDOGJaJJqOqhcKd9jwa07gDFV3R5Zhar3MHwFrN5V4N2BU9d2gykpP8CpSFK2zjeTQAAJoO7tVA00a4Q5PB82hA8zRjdZZ+t/grq8sAUAvcq4GmjWGZaDKqWiicIYwAADQN3KuBpo1whyaF59EAAGjauFcDTRfDMgEAAADAAQh3AAAAAOAADMsEAFjqpKR4xcZyOwIAoL64mwIALDUutQPr3AEA0AAIdwAASxUGy+QPlFldBgAAtmdZuHvsscfk8Xg0adIkq0oAADQBj3y9WS6XNL1HqtWlAABga1GfUKWgoEB33XWX/vKXv0T7rQEAAADAsaIe7pYuXarjjz9eV111VbTfGgAAAAAcK+rhbuTIkbruuuvk8Xii/dYAAAAA4FiN9szde++9p5kzZ1bYduKJJ+qFF16o13Hbtk2o1/fbSUpKotUloBZoJ3ugnZoul+vg/2kje6Cd7IF2avpoI3uwWzs1WrgbNGiQBg0a1ODH3bOnUOGwafDjNjVMC24PtJM90E5N2ymtEuT3+2gjG+BasgfaqemjjeyhKbaT2+2qtrOLpRAAAJa66IT2TfIGCgCA3RDuAACWyikOqKywhBsSAAD1ZNm9lPXtAACSNHf9Fta5AwCgAUR9tkwAAAAAQMNjFIzNrM3K1aIV2crNL1Fykl8Z6R2VlppsdVkAAAAALEa4s5G1WbnKXLJRHo9b8X6v8ooCylyyUZIIeAAAAEAzx7BMG1m0Ilsej1uxPo9cLpdifR55PG4tWpFtdWkAAAAALEbPnY3k5pco3l+xyWK8buXml1hUEQDU3xltExUXF2N1GQAA2B7hzkaSk/zKKwoo1ueJbAuUhZWc5LewKgCon6Gd2rHOHQAADYBwZyMZ6R2VuWSjSnWwxy5QFlYoFFZGekerSwOaFSY2aljZBcUq8LqUaHUhAADYHOHORsr/8dic/1HJP6phNSY2angvfL+dde4AAGgAhLsmpjy87C0MqE1CTKXwkpaa3Gz/Ack/qtEUHDqxkSTF+jwq/e92zkMAAGAlZstsQsrDS15RQIlxP4WXtVm5VpfWJDBbKJqC3PwSxXgr/tXJxEYAAKApINw1IYSX6vGPajQFyUl+BcrCFbYxsREAAGgKGJbZhLDUQfWYLbTh8QzjkWNiIwAA0FTRc9eE0CNQvYz0jgqFwioNhmSMUWkwxD+q6+HQYcCHPsPIMODqpaUma/wFXdSqRYwOlJSpVYsYjb+gC6G4Hn7VLkn9O6VYXQYAALZHz10TcmiPgNfjIrz8DLOFNiwmBqm75jyxUWMYcGwy69wBANAACHdNyKHhZV9hQK0PM1tmc8c/qhsOw4DRVGzIK9JOE9ZRLgaTAABQH4S7JqY8vPBbbDQ2nmFEU/H3/+xknTsAABoAvyYFmimeYQQAAHAWeu6AZopnGAEAAJyFcAc0YzzDCAAA4BwMywQAAAAAB6DnDgBgqb5HtVaLhFirywAAwPYIdwAAS/U9ug0zBAMA0AAIdwAAS63ZU6CWwaBO8PmsLgUAAFvjmTsAgKXe2rxbf/sm2+oyAACwPcIdAAAAADgA4Q4AAAAAHIBwBwAAAAAOQLgDAAAAAAdgtkwAgKUGHtNWCS39VpcBAIDtEe6AKqzNytWiFdnKzS9RcpJfGekdlZaabHVZgOOkt2/FOncAADQAwh1wGGuzcpW5ZKM8Hrfi/V7lFQWUuWSjJBHwgAa2YleeEopLdGocvXcAANQHz9wBh7FoRbY8HrdifR65XC7F+jzyeNxatIK1uICG9v62PZr/3TarywAAwPYId8Bh5OaXKMZb8fKI8bqVm19iUUUAAABA9Qh3wGEkJ/kVKAtX2BYoCys5iWFjAAAAaJoId8BhZKR3VCgUVmkwJGOMSoMhhUJhZaR3tLo0AAAA4LCYUAU4jPJJU5gtEwAAAHZBuAOqkJaaTJgDomBEp3ZqyTp3AADUG8MyAQCW+kXbRJ11dBurywAAwPbouQMAWOqj7XvVYn+RerZsYXUpAADYGj13AABLfbRzn97L2ml1GQAA2B7hDgAAAAAcgHAHAAAAAA5AuAMAAAAAByDcAQAAAIADMFsmAMBSY048SklJcVaXAQCA7dFzBwCwVNdWLXR6uySrywAAwPbouQMAWGrx1ly12Ltfvdu0tLoUAABsjXAHALDUZ7vz5XKJcAcAQD0xLBMAAAAAHIBwBwAAAAAOQLgDAAAAAAcg3AEAAACAAzChCgDAUleedLRat46XyozVpQAAYGv03AEALNUxMU4ntk6wugwAAGyPnjsAgKXe3rxbcbvzdF67VlaXAgCArdFzBwCw1Oo9BVq+bY/VZQAAYHuEOwAAAABwAMIdAAAAADgA4Q4AAAAAHIBwBwAAAAAOwGyZAABLXX/ycWrdpoVUHLS6FAAAbI2eOwCApVLiYtQhwW91GQAA2B49dwAAS73xwy75d+zVkA5trC4FAABbo+cOAGCpdfsKtXrnPqvLAADA9gh3AAAAAOAAhDsAAAAAcADCHQAAAAA4AOEOAAAAAByA2TIBAJa69fROSk5OVEl+sdWlAABga/TcAQAsleDzKjGG3zUCAFBfhDsAgKVeydqhZ778j9VlAABge4Q7AIClvs8/oHU5+60uAwAA2yPcAQAAAIADEO4AAAAAwAGiHu6++OILXXzxxRoxYoSuuOIKbdu2LdolAAAAAIDjRD3c3XHHHXrooYf01ltvadiwYXrooYeiXQIAAAAAOE5U554OBAK6+eab1a1bN0lS165d9be//S2aJTSK577bWmnb6W0S9Kt2rRQIhfXX77dX2n9Gckv9MrmlioIhvZy1o9L+C4LtdbzPp7zSoF77YVel/X2OaqWTWyUopzigNzfvrrS/X4c26pwUr+0HSvVOdk6l/QOOaatOiXHaXFCsxdv2qLA4qL37S1QWCsvrcev8o1qrb9ejtCn/gP61Y2+l7x/ZqZ1S4mK0Pq9Qn+zMq7R/9Ant1SrWp7V7CrQiJ7/S/ktTO6iFz6MvcvdrdW7liRSuOOloxXjc+mx3nr7eW1hp/2+7HStJ+njHPn2XX1Rhn8/t0pVdjpEkfbB9j7L2V1w7K97r1vjOR0uS3t+Sq+yikgr7k3xeXZJ6lCTp7ewc7ThQWmF/st+nUce3lyS9+PVmbc07UGF/h/hYDe2YIkl6NWun8oNlFfZ3bOHXwOOSJUmZm7brQFm4wv7UlnHqf3RbSdILG7cpGDYV9ndLaqFfd2gtqXHOvfSUJKW1TYzaufdzQzqm6Oj42AY993xZHgWDIUnOOfcW/LhLuSXBCvvteu51iI+Vz+fR2j0Fjjv3DsW51/TOvXJO+3vv0L/zJM69pnjulbeR0869n7P7uXddSmKlmpq6qIa7mJgYjRgxQpIUDof11FNP6fzzzz+iY7Rtm9AYpdWLL8tTaVtCgl8pKYkqDYXl+7Hy/sTEg/v9gTL5sivvl6SUlER5igPybc2ttC+pZZxSUhIVKiyRb3vl709qFaeU5EQV7/fIt6Py/lat45XSOkH5XpdKftytnH3FkkvyeNwKhY3e/3yLjm/bUkkpcfLlVv7+Nm1aKCXBr+3hkHx7Cirtb9s2QW3iYtQyGJQvr/LF2jY5QYkxXiWWBuT72cUqSckpiYr1uJVwoES+gsoLG6f892JrUXBAvgMVL9YYj/un/XmF8hUHKuyP9Xkj++P37JcvUPFGEev/aX9cTp58P7tR+P2+yH7t3Cufr+LPJy7up/2x23LlU8UbRXx8zE/7s30Kuioev0V8bGR/zI9eKVTxRtQi4af9jXHutWzpj9q559udV2l/69bxSmkZrxyXadBzr7ydnHLu+Xfule9n5wbnXtM89yL7nXLu+X2cez/f3wTPvUPvTY459xz2957P53HkuVdhv83PvUNrsAuXMcbU/GVH7r333tPMmTMrbDvxxBP1wgsvKBAI6M4771R+fr6efvpp+Xy+Wh93z55ChcONUnKTkpKSqJycyhdRY5j98mrlFQUUe8iNoDQYUqsWMZpy6RlRqcGuotlOqDvaqWn768Ztionxatx/fzuPpotryR5op6aPNrKHpthObrer2s6uRuu5GzRokAYNGlRpe1FRka6//nq1atVKc+fOPaJgh8aRm1+ieH/FUyHG61ZufkkV3wEADWdzYYlcLkmEOwAA6sWSCVU6deqkxx57TDExMdF+exxGcpJfgZ+NQQ+UhZWc5LeoIgAAAABHKqrP3H377bdaunSpOnfurFGjRkmS2rVrp+eeey6aZeBnMtI7KnPJRpXqYI9doCysUCisjPSOVpcGAAAAoJaiGu5OOeUUbdiwIZpviVpISz04i9SiFdnKzS9RcpJfGekdI9sBAAAANH1RDXdoutJSkwlzACzhdklul8vqMgAAsD3CHQDAUvf0SG2SM5IBAGA3UZ9QBQAAAADQ8Oi5AwBY6s/fbVVMlke/Se1gdSkAANga4Q4AYKntB0rFI3cAANQfwzIBAAAAwAEIdwAAAADgAIQ7AAAAAHAAnrkDAFjK53bJ4+Z3jQAA1BfhDgBgqWndT2SdOwAAGgC/KgUAAAAAB6DnDgBgqbnfbpHP59a1Jx1jdSkAANga4Q4AYKmckoBcpVZXAQCA/TEsEwAAAAAcgHAHAAAAAA5AuAMAAAAAB+CZOwCApeI8brk9/K4RAID6ItwBACx1xy9OYJ07AAAaAL8qBQAAAAAHoOcOAGCpJ9dtltfj0fXdjrW6FAAAbI1wBwCw1L7SMrlcZVaXAQCA7TEsEwAAAAAcgHAHAAAAAA5AuAMAAAAAB+CZOwCApRJ9HnlY5w4AgHoj3AEALDX59ONZ5w4AgAbAr0oBAAAAwAHouQMAWOrRr3+Ux+PW707paHUpAADYGuEOAGCpgmBIrrKQ1WUAAGB7DMsEAAAAAAcg3AEAAACAAxDuAAAAAMABeOYOAGCp1rFeeT0eq8sAAMD2CHcAAEtNOrUT69wBANAAGJYJAAAAAA5Azx0AwFJ/XPOD3B63bjutk9WlAABga4Q7AIClikNhucJhq8sAAMD2GJYJAAAAAA5AuAMAAAAAByDcAQAAAIAD8MwdAMBSKf4Y+Xz8rhEAgPoi3AEALHX9Kcexzh0AAA2AX5UCAAAAgAPQcwcAsNTMr/4jj9utKWnHW10KAAC2RrgDAFgqGDYqMyGrywAAwPYYlgkAAAAADkC4AwAAAAAHINwBAAAAgAPwzB0AwFJHx8cqxuexugwAAGyPcAcAsNS13Y5lnTsAABoAwzIBAAAAwAHouQMAWOqhL7Pkdrl0V/cTrS4FAABbI9wBACwVNpKRsboMAABsj2GZAAAAAOAAhDsAAAAAcADCHQAAAAA4AM/cAQAs1SnBr5gYbkcAANQXd1MAgKWu6HIM69wBANAAGJYJAAAAAA5Azx0AwFIzVmfJ5ZKm90i1uhQAAGyNnjsAAAAAcADCHQAAAAA4AOEOAAAAAByAcAcAAAAADsCEKgAAS52UFK/YWG5HAADUF3dTAIClxqV2YJ07AAAaAOEOAGCpwmCZ/IEyq8sAAMD2CHcAAEs98vVm1rkDAKABMKEKAAAAADgA4Q4AAAAAHIBwBwAAAAAOQLgDAAAAAAdgQhUAgKVObZ0gv99ndRkAANhe1MPdqlWr9Pvf/17BYFDHHHOM/vCHPygpKSnaZQAAmoiLTmjPOncAADSAqA/LnDZtmmbPnq2FCxeqc+fOev7556NdAgCgCckpDmhHYYnVZQAAYHtR77l799135fP5FAwGtWvXLnXt2jXaJQAAmpC567ewzh0AAA0g6j13Pp9PGzZsUN++fbVixQoNGTIk2iUAAAAAgOO4jDGmMQ783nvvaebMmRW2nXjiiXrhhRcir+fNm6c333xT8+bNa4wSAAAAAKDZaLRwdzilpaX6+OOPdf7550uSDhw4oN69e+vLL7+s9TH27ClUOBy1ki3D5AL2QDvZA+3U9NFG9kA72QPt1PTRRvbQFNvJ7XapbduEqvdHsRZ5vV498MAD+uabbyQd7N0744wzolkCAAAAADhSVCdU8Xg8evTRR3XvvfcqFAqpffv2evjhh6NZAgAAAAA4UtRny+zZs6fmz58f7bcFAAAAAEeL+myZAAAAAICGR7gDAAAAAAcg3AEAAACAAxDuAAAAAMABCHcAAAAA4ACEOwAAAABwAMIdAAAAADgA4Q4AAAAAHIBwBwAAAAAOQLgDAAAAAAcg3AEAAACAAxDuAAAAAMABCHcAAAAA4ACEOwAAAABwAMIdAAAAADgA4Q4AAAAAHIBwBwAAAAAOQLgDAAAAAAfwWl3AkXK7XVaXEDXN6bPaGe1kD7RT00cb2QPtZA+0U9NHG9lDU2unmupxGWNMlGoBAAAAADQShmUCAAAAgAMQ7gAAAADAAQh3AAAAAOAAhDsAAAAAcADCHQAAAAA4AOEOAAAAAByAcAcAAAAADkC4AwAAAAAHINwBAAAAgAMQ7qJk4cKFGjx4sAYMGKDMzMxK+9evX68LL7xQAwcO1N13362ysjJJ0hdffKGLL75YI0aM0BVXXKFt27ZJkj7//HOlp6drxIgRGjFihKZNmxbVz+NUdW2nBQsWqE+fPpH2ePTRRyVJ+/fv13XXXadBgwZp/PjxysnJierncaK6tNGePXsibTNixAj1799fPXr0kMS11FhqaqdyU6ZM0fz58yOvt2/frvHjxysjI0PXX3+9ioqKJHEtNZa6thP3puipaxtxX4quurQT96boq6md/vnPf2rEiBEaPny4brjhBuXn50uy2b3JoNHt3LnT9OvXz+zbt88UFRWZYcOGme+//77C1wwZMsR8+eWXxhhjpk2bZjIzM40xxvTr18+sX7/eGGPMa6+9ZiZOnGiMMeb55583Tz/9dPQ+RDNQn3aaMWOGWbhwYaVjPvDAA+aZZ54xxhizYMECc/PNNzfqZ3C6+rRRuVAoZC677DLzj3/8wxjDtdQYatNOO3fuNBMmTDBpaWnmjTfeiGy/7rrrzNtvv22MMeapp54ys2fPNsZwLTWG+rQT96boqE8bcV+Knvq0UznuTY2vpnYqKCgwvXv3Njt37jTGGPPYY4+ZBx980Bhjr3sTPXdRsHz5cv3qV79Sq1atFB8fr4EDB2rRokWR/du2bVNJSYm6d+8uSbrwwgu1aNEiBQIB3XzzzerWrZskqWvXrtqxY4ck6euvv9Ynn3yiYcOGaeLEiZHtqLu6tpN0sD0WLFigYcOG6fbbb4/8pufDDz/UsGHDJElDhw7Vv//9bwWDweh+MAepTxuVe+ONNxQXFxdpF66lhldTO0kHf3t63nnnadCgQZFtwWBQK1eu1MCBAyVVbD+upYZX13bi3hQ9dW0jiftSNNWnncpxb2p8NbVTMBjUfffdp/bt20v66e82u92bCHdRsHv3bqWkpERet2vXTrt27apyf0pKinbt2qWYmBiNGDFCkhQOh/XUU0/p/PPPlyQlJibq8ssv18KFC9W3b19Nnjw5Sp/GueraTuV/vuGGG/SPf/xDHTp00IwZMyp9j9frVUJCgvbu3RuNj+NI9WkjSQqFQnr66ad12223RbZxLTW8mtpJkq699lqNHj26wrZ9+/YpISFBXq9XUsX241pqeHVtJ+5N0VPXNpK4L0VTfdpJ4t4ULTW1U+vWrXXBBRdIkkpKSvTss8/q/PPPt929iXAXBeFwWC6XK/LaGFPhdU37A4GAbr/9dpWVlWnChAmSpBkzZmjAgAGSpHHjxmnTpk0qKCho7I/iaPVppz/96U/65S9/KZfLpWuvvVYff/zxYd/DGCO3m8uurup7LX388cc6/vjj1bVr18g2rqWGV1M7VOVwX1fV93Et1V9d26kc96bGV5824r4UPfW9lrg3RUdt26mgoEDXXXedunXrplGjRtnu3sTVHAVHHXVUhQcsc3Jy1K5duyr35+bmRvYXFRXp2muvVVlZmebOnSufz6dwOKy5c+cqFApVeB+Px9PIn8TZ6tpOBQUFeuGFFyLbjTGRtmjXrp1yc3MlSWVlZSoqKlKrVq0a94M4WH2uJengg9KDBw+OvOZaahw1tVNV2rRpo4KCgkh7HPp9XEsNr67tJHFvipa6thH3peiqz7UkcW+Kltq00+7du3XppZeqa9euevjhhyXZ795EuIuCs88+W59++qn27t2r4uJiLV68WOecc05k/zHHHKPY2Fh98cUXkqS33norsv+OO+5Qp06d9NhjjykmJkaS5Ha7tWTJEr3//vuSpDfffFO/+MUvFB8fH+VP5ix1baf4+Hj9+c9/1po1ayRJf/vb3yLd+n379tWbb74pSXr33XfVs2dP+Xy+6H4wB6nPtSRJX331lXr27Bl5zbXUOGpqp6r4fD717NlT7777rqSD7VH+fVxLDa+u7SRxb4qWurYR96Xoqs+1JHFvipaa2ikUCmnixIkaNGiQ7r777kjvnO3uTVGewKXZ+sc//mGGDBliBgwYYJ599lljjDHXXnutWbt2rTHGmPXr15uLLrrIDBw40Nx6662mtLTUrFu3znTp0sUMHjzYDB8+3AwfPtxce+21xhhjNm7caMaMGWMGDx5sLrvsMrN9+3bLPpuT1KWdjDFm5cqVZuTIkSYjI8NMnDjR7N+/3xhjzL59+8yECRPM4MGDzZgxY8yWLVus+WAOUtc2MsaYtLQ0U1JSUuF4XEuNo6Z2Kjd16tQKM8dt3brVXHbZZWbQoEHm6quvNnl5ecYYrqXGUpd24t4UXXW9lrgvRVdd28kY7k3RVF07LV682HTt2jXy99rw4cPNXXfdZYyx173JZYwx1sZLAAAAAEB9MSwTAAAAAByAcAcAAAAADkC4AwAAAAAHINwBAAAAgAMQ7gAAAADAAQh3AABH2bp1q3r06GF1GQAARB3hDgAAAAAcwGt1AQAAREMgENCcOXO0cuVKhUIhnXLKKbrnnnv01Vdf6Q9/+IMWLlwoSdq/f7/OO+88/fOf/1RJSYlmzJihHTt2KBgMasiQIZo4caK2bt2q8ePHKzU1Vdu2bdNLL72k+fPna+nSpSopKVFxcbGmTp2qCy64QMXFxbrvvvu0Zs0aJSYmqnPnzpKkWbNmadeuXYc9PgAAdUHPHQCgWXj22Wfl8Xg0f/58/eMf/1C7du00Z84c9e7dW0VFRfr6668lSW+//bb69u2rpKQk3XHHHbrooos0f/58vf7661q+fLneffddSdLOnTt1ww036P3331cwGNTy5cv10ksvaeHChZo8ebKeeOIJSdL//u//KhQK6b333tMLL7ygb7/9NlJTdccHAOBI0XMHAGgWPvzwQxUUFGj58uWSpGAwqLZt28rlcumiiy7SggULdPrpp2v+/PmaMmWKDhw4oJUrVyo/P1+PP/64JOnAgQP67rvvlJaWJq/Xq+7du0uSjjnmGM2ePVsLFy7U5s2btWbNGhUVFUmSPvroI02bNk1ut1sJCQkaNWqUNmzYUO3xBw8eHP0fEADA9gh3AIBmIRwO66677lLfvn0lSUVFRSotLZUkXXzxxRo1apRGjx6tgoICnXXWWSosLJQxRvPmzVNcXJwkae/evYqNjdW+ffsUExMjr/fgbXTdunW64YYbdOWVV6p3794688wz9cADD0iSvF6vjDGROtxud6Seqo4PAEBdMCwTANAs9OnTR5mZmQoEAgqHw5o+fboeeeQRSVL79u2Vlpame++9VxdffLEkKSEhQd27d9df/vIXSQefxRs3bpyWLl1a6dgrV67UaaedpquuukpnnXWWli5dqlAoJEnq27ev3njjDYXDYRUXF+vtt9+Wy+U6ouMDAFAbhDsAgOMcOHBAPXr0qPDfkCFDdMwxx2jUqFEaPHiwjDG68847I98zevRorV+/XqNGjYpsmzNnjtasWaNhw4Zp9OjRGjp0qIYPH17p/YYOHap9+/Zp0KBBGjx4sOLj45Wfn6/CwkJNmDBBsbGxGjZsmK666iq1bdtWfr//iI4PAEBtuMyhY0UAAECDeuedd5SQkKC+ffsqHA5r0qRJ6t27ty699FKrSwMAOAzhDgCARrRx40bde++9Ki4uVjAYVHp6uu666y75fD6rSwMAOAzhDgAAAAAcgGfuAAAAAMABCHcAAAAA4ACEOwAAAABwAMIdAAAAADgA4Q4AAAAAHIBwBwAAAAAO8P+EhsnkXVX8ewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temp = df_all\n",
    "df_temp = df_temp[df_temp.city != \"BREMERHAVEN\"]\n",
    "df_temp = df_temp[df_temp.city != \"MUELHEIMRUHR\"]\n",
    "df_temp = df_temp[df_temp.city != \"BRESLAU\"]\n",
    "df_temp = df_temp[df_temp.city != \"Hessen_50k\"]\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_temp).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_temp.total_tb, df_temp.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities over 100000 Plus Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 2.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.6])\n",
    "plt.show()\n",
    "\n",
    "influence = reg.get_influence()\n",
    "inf_sum = influence.summary_frame()\n",
    "\n",
    "student_resid = influence.resid_studentized_external\n",
    "(cooks, p) = influence.cooks_distance\n",
    "(dffits, p) = influence.dffits\n",
    "leverage = influence.hat_matrix_diag\n",
    "\n",
    "print ('\\n')\n",
    "plt.figure(figsize = (15, 10))\n",
    "sns.regplot(x = leverage, y = reg.resid_pearson,  fit_reg=False)\n",
    "plt.plot([.02, .2], [2, 2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([.02, .2], [-2, -2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/40, 4/40], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/40, 4/40], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.title('Leverage vs. Studentized Residuals- Bivariate Regression')\n",
    "plt.xlabel('Leverage')\n",
    "plt.ylabel('Studentized Residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to make sure that our previous results with total mortality aren't too driven by outliers, either. We'll start with results with just larger cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.136\n",
      "Model:                            OLS   Adj. R-squared:                  0.101\n",
      "Method:                 Least Squares   F-statistic:                     3.924\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):             0.0587\n",
      "Time:                        22:29:45   Log-Likelihood:                -88.596\n",
      "No. Observations:                  27   AIC:                             181.2\n",
      "Df Residuals:                      25   BIC:                             183.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -2.3665      7.233     -0.327      0.746     -17.264      12.531\n",
      "total_tb       8.7319      4.408      1.981      0.059      -0.346      17.810\n",
      "==============================================================================\n",
      "Omnibus:                       13.229   Durbin-Watson:                   2.366\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               12.443\n",
      "Skew:                           1.427   Prob(JB):                      0.00199\n",
      "Kurtosis:                       4.706   Cond. No.                         12.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Studentized Residuals')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_100000).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000.total_tb, df_100000.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities over 100000\")\n",
    "plt.plot([.9, 2.4], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.4])\n",
    "plt.show()\n",
    "\n",
    "influence = reg.get_influence()\n",
    "inf_sum = influence.summary_frame()\n",
    "\n",
    "student_resid = influence.resid_studentized_external\n",
    "(cooks, p) = influence.cooks_distance\n",
    "(dffits, p) = influence.dffits\n",
    "leverage = influence.hat_matrix_diag\n",
    "\n",
    "print ('\\n')\n",
    "plt.figure(figsize = (15, 10))\n",
    "sns.regplot(x = leverage, y = reg.resid_pearson,  fit_reg=False)\n",
    "plt.plot([.02, .2], [2, 2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([.02, .2], [-2, -2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/44, 4/44], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/44, 4/44], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.title('Leverage vs. Studentized Residuals- Bivariate Regression')\n",
    "plt.xlabel('Leverage')\n",
    "plt.ylabel('Studentized Residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is are two notable outliers, corresponding with MUELHEIMRUHR and BRESLAU. Removing these, we get the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.116\n",
      "Model:                            OLS   Adj. R-squared:                  0.077\n",
      "Method:                 Least Squares   F-statistic:                     3.015\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):             0.0959\n",
      "Time:                        22:29:45   Log-Likelihood:                -70.851\n",
      "No. Observations:                  25   AIC:                             145.7\n",
      "Df Residuals:                      23   BIC:                             148.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.5166      5.079      0.299      0.768      -8.990      12.023\n",
      "total_tb       5.4256      3.124      1.737      0.096      -1.038      11.889\n",
      "==============================================================================\n",
      "Omnibus:                        1.849   Durbin-Watson:                   2.085\n",
      "Prob(Omnibus):                  0.397   Jarque-Bera (JB):                1.141\n",
      "Skew:                           0.523   Prob(JB):                        0.565\n",
      "Kurtosis:                       3.006   Cond. No.                         13.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Studentized Residuals')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temp = df_100000\n",
    "df_temp = df_temp[df_temp.city != \"MUELHEIMRUHR\"]\n",
    "df_temp = df_temp[df_temp.city != \"BRESLAU\"]\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_temp).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_temp.total_tb, df_temp.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities over 100000\")\n",
    "plt.plot([.9, 2.4], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.4])\n",
    "plt.show()\n",
    "\n",
    "influence = reg.get_influence()\n",
    "inf_sum = influence.summary_frame()\n",
    "\n",
    "student_resid = influence.resid_studentized_external\n",
    "(cooks, p) = influence.cooks_distance\n",
    "(dffits, p) = influence.dffits\n",
    "leverage = influence.hat_matrix_diag\n",
    "\n",
    "print ('\\n')\n",
    "plt.figure(figsize = (15, 10))\n",
    "sns.regplot(x = leverage, y = reg.resid_pearson,  fit_reg=False)\n",
    "plt.plot([.02, .3], [2, 2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([.02, .3], [-2, -2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/42, 4/42], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/42, 4/42], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.title('Leverage vs. Studentized Residuals- Bivariate Regression')\n",
    "plt.xlabel('Leverage')\n",
    "plt.ylabel('Studentized Residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After removing those outliers, the relationship still stands, though it is not as significant as before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to do the same sort of analysis with the dataset of cities over 100000 and grouped cities combined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.109\n",
      "Model:                            OLS   Adj. R-squared:                  0.084\n",
      "Method:                 Least Squares   F-statistic:                     4.411\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):             0.0428\n",
      "Time:                        22:29:46   Log-Likelihood:                -127.63\n",
      "No. Observations:                  38   AIC:                             259.3\n",
      "Df Residuals:                      36   BIC:                             262.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.6105      6.048     -0.101      0.920     -12.876      11.654\n",
      "total_tb       7.7923      3.710      2.100      0.043       0.268      15.317\n",
      "==============================================================================\n",
      "Omnibus:                       12.999   Durbin-Watson:                   2.370\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               13.068\n",
      "Skew:                           1.267   Prob(JB):                      0.00145\n",
      "Kurtosis:                       4.354   Cond. No.                         11.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Studentized Residuals')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_100000_grouped).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_100000_grouped.total_tb, df_100000_grouped.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities Over 100000 Plus Grouped\")\n",
    "plt.plot([.6, 2.5], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.5])\n",
    "plt.show()\n",
    "\n",
    "influence = reg.get_influence()\n",
    "inf_sum = influence.summary_frame()\n",
    "\n",
    "student_resid = influence.resid_studentized_external\n",
    "(cooks, p) = influence.cooks_distance\n",
    "(dffits, p) = influence.dffits\n",
    "leverage = influence.hat_matrix_diag\n",
    "\n",
    "print ('\\n')\n",
    "plt.figure(figsize = (15, 10))\n",
    "sns.regplot(x = leverage, y = reg.resid_pearson,  fit_reg=False)\n",
    "plt.plot([.02, .2], [2, 2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([.02, .2], [-2, -2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/44, 4/44], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/44, 4/44], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.title('Leverage vs. Studentized Residuals- Bivariate Regression')\n",
    "plt.xlabel('Leverage')\n",
    "plt.ylabel('Studentized Residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three notable outliers here are Breslau, Muelheimruhr, and Hessen_50k. Removing these, we get the following results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.103\n",
      "Model:                            OLS   Adj. R-squared:                  0.076\n",
      "Method:                 Least Squares   F-statistic:                     3.788\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):             0.0602\n",
      "Time:                        22:29:46   Log-Likelihood:                -102.62\n",
      "No. Observations:                  35   AIC:                             209.2\n",
      "Df Residuals:                      33   BIC:                             212.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.2073      4.178      0.528      0.601      -6.293      10.707\n",
      "total_tb       5.0302      2.584      1.946      0.060      -0.228      10.288\n",
      "==============================================================================\n",
      "Omnibus:                        1.024   Durbin-Watson:                   2.192\n",
      "Prob(Omnibus):                  0.599   Jarque-Bera (JB):                1.020\n",
      "Skew:                           0.361   Prob(JB):                        0.600\n",
      "Kurtosis:                       2.577   Cond. No.                         11.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Studentized Residuals')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temp = df_100000_grouped\n",
    "df_temp = df_temp[df_temp.city != \"MUELHEIMRUHR\"]\n",
    "df_temp = df_temp[df_temp.city != \"BRESLAU\"]\n",
    "df_temp = df_temp[df_temp.city != \"Hessen_50k\"]\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_temp).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_temp.total_tb, df_temp.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities over 100000 Plus Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 2.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.6])\n",
    "plt.show()\n",
    "\n",
    "influence = reg.get_influence()\n",
    "inf_sum = influence.summary_frame()\n",
    "\n",
    "student_resid = influence.resid_studentized_external\n",
    "(cooks, p) = influence.cooks_distance\n",
    "(dffits, p) = influence.dffits\n",
    "leverage = influence.hat_matrix_diag\n",
    "\n",
    "print ('\\n')\n",
    "plt.figure(figsize = (15, 10))\n",
    "sns.regplot(x = leverage, y = reg.resid_pearson,  fit_reg=False)\n",
    "plt.plot([.02, .2], [2, 2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([.02, .2], [-2, -2], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/40, 4/40], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.plot([4/40, 4/40], [3, -3], color = 'c', linestyle = 'dashed')\n",
    "plt.title('Leverage vs. Studentized Residuals- Bivariate Regression')\n",
    "plt.xlabel('Leverage')\n",
    "plt.ylabel('Studentized Residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the results are slightly less significant, which could be just from a smaller N, but the general relationship still holds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standard Deviation Stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to know how many sd in total_tb change will lead to one sd change in change_votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.127\n",
      "Model:                            OLS   Adj. R-squared:                  0.105\n",
      "Method:                 Least Squares   F-statistic:                     5.952\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):             0.0191\n",
      "Time:                        22:29:46   Log-Likelihood:                -142.42\n",
      "No. Observations:                  43   AIC:                             288.8\n",
      "Df Residuals:                      41   BIC:                             292.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.5314      5.188     -0.102      0.919     -11.008       9.945\n",
      "total_tb       7.6848      3.150      2.440      0.019       1.323      14.046\n",
      "==============================================================================\n",
      "Omnibus:                       15.705   Durbin-Watson:                   2.284\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               17.848\n",
      "Skew:                           1.316   Prob(JB):                     0.000133\n",
      "Kurtosis:                       4.743   Cond. No.                         11.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Standard Deviation of total_tb: 0.3291827295588207\n",
      "Standard Deviation of change_votes: 6.885348060226168\n",
      "SD of total_tb needed for one SD increase in change_votes: 2.721800488960438\n",
      "SD of change_votes from one SD increase in total_tb: 0.3674038578712796\n"
     ]
    }
   ],
   "source": [
    "df_temp = df_all\n",
    "df_temp = df_temp[df_temp.city != \"BREMERHAVEN\"]\n",
    "\n",
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_temp).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "print(\"Standard Deviation of total_tb: \" + str(np.std(df_temp.total_tb)))\n",
    "print(\"Standard Deviation of change_votes: \" + str(np.std(df_temp.change_votes)))\n",
    "\n",
    "print(\"SD of total_tb needed for one SD increase in change_votes: \" + str(np.std(df_temp.change_votes) / (7.6848 * np.std(df_temp.total_tb))))\n",
    "print(\"SD of change_votes from one SD increase in total_tb: \" + str(((7.6848 * np.std(df_temp.total_tb)) / np.std(df_temp.change_votes))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Fraction Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           change_votes   R-squared:                       0.127\n",
      "Model:                            OLS   Adj. R-squared:                  0.105\n",
      "Method:                 Least Squares   F-statistic:                     5.952\n",
      "Date:                Wed, 11 May 2022   Prob (F-statistic):             0.0191\n",
      "Time:                        22:29:46   Log-Likelihood:                -142.42\n",
      "No. Observations:                  43   AIC:                             288.8\n",
      "Df Residuals:                      41   BIC:                             292.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.5314      5.188     -0.102      0.919     -11.008       9.945\n",
      "total_tb       7.6848      3.150      2.440      0.019       1.323      14.046\n",
      "==============================================================================\n",
      "Omnibus:                       15.705   Durbin-Watson:                   2.284\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               17.848\n",
      "Skew:                           1.316   Prob(JB):                     0.000133\n",
      "Kurtosis:                       4.743   Cond. No.                         11.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_all = pd.read_csv(\"CITIES_OVER_100000_AND_SMALLER_CITIES_AND_GROUPED.csv\")\n",
    "df_all = df_all[df_all.city != \"BREMERHAVEN\"]\n",
    "reg = sm.formula.ols(\"change_votes ~ total_tb\", data = df_all).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_all.total_tb, df_all.change_votes)\n",
    "plt.xlabel(\"Ratio Mort TB\")\n",
    "plt.ylabel(\"Proportion Change in Votes\")\n",
    "plt.title(\"Ratio Mort TB vs Change Votes for Cities over 100000 Plus Smaller Cities Plus Grouped\")\n",
    "plt.plot([.6, 2.6], [reg.params[0] + reg.params[1] * .6, reg.params[0] + reg.params[1] * 2.6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "PatsyError",
     "evalue": "Error evaluating factor: NameError: name 'overMt1' is not defined\n    change_votes ~ Mt + overMt1 + MtxoverMt1\n                        ^^^^^^^",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\patsy\\compat.py\u001b[0m in \u001b[0;36mcall_and_wrap_exc\u001b[1;34m(msg, origin, f, *args, **kwargs)\u001b[0m\n\u001b[0;32m     35\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     37\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\patsy\\eval.py\u001b[0m in \u001b[0;36meval\u001b[1;34m(self, expr, source_name, inner_namespace)\u001b[0m\n\u001b[0;32m    164\u001b[0m         \u001b[0mcode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msource_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"eval\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 165\u001b[1;33m         return eval(code, {}, VarLookupDict([inner_namespace]\n\u001b[0m\u001b[0;32m    166\u001b[0m                                             + self._namespaces))\n",
      "\u001b[1;32m<string>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'overMt1' is not defined",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mPatsyError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-a368fd525e48>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mreg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformula\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mols\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"change_votes ~ Mt + overMt1 + MtxoverMt1\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_all\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py\u001b[0m in \u001b[0;36mfrom_formula\u001b[1;34m(cls, formula, data, subset, drop_cols, *args, **kwargs)\u001b[0m\n\u001b[0;32m    167\u001b[0m             \u001b[0mmissing\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'raise'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m         tmp = handle_formula_data(data, None, formula, depth=eval_env,\n\u001b[0m\u001b[0;32m    170\u001b[0m                                   missing=missing)\n\u001b[0;32m    171\u001b[0m         \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmissing_idx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdesign_info\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtmp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\statsmodels\\formula\\formulatools.py\u001b[0m in \u001b[0;36mhandle_formula_data\u001b[1;34m(Y, X, formula, depth, missing)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata_util\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_using_pandas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m             result = dmatrices(formula, Y, depth, return_type='dataframe',\n\u001b[0m\u001b[0;32m     64\u001b[0m                                NA_action=na_action)\n\u001b[0;32m     65\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\patsy\\highlevel.py\u001b[0m in \u001b[0;36mdmatrices\u001b[1;34m(formula_like, data, eval_env, NA_action, return_type)\u001b[0m\n\u001b[0;32m    307\u001b[0m     \"\"\"\n\u001b[0;32m    308\u001b[0m     \u001b[0meval_env\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mEvalEnvironment\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcapture\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0meval_env\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreference\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 309\u001b[1;33m     (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env,\n\u001b[0m\u001b[0;32m    310\u001b[0m                                       NA_action, return_type)\n\u001b[0;32m    311\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlhs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\patsy\\highlevel.py\u001b[0m in \u001b[0;36m_do_highlevel_design\u001b[1;34m(formula_like, data, eval_env, NA_action, return_type)\u001b[0m\n\u001b[0;32m    162\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdata_iter_maker\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    163\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 164\u001b[1;33m     design_infos = _try_incr_builders(formula_like, data_iter_maker, eval_env,\n\u001b[0m\u001b[0;32m    165\u001b[0m                                       NA_action)\n\u001b[0;32m    166\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdesign_infos\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\patsy\\highlevel.py\u001b[0m in \u001b[0;36m_try_incr_builders\u001b[1;34m(formula_like, data_iter_maker, eval_env, NA_action)\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformula_like\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModelDesc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0meval_env\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mEvalEnvironment\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m         return design_matrix_builders([formula_like.lhs_termlist,\n\u001b[0m\u001b[0;32m     67\u001b[0m                                        formula_like.rhs_termlist],\n\u001b[0;32m     68\u001b[0m                                       \u001b[0mdata_iter_maker\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\patsy\\build.py\u001b[0m in \u001b[0;36mdesign_matrix_builders\u001b[1;34m(termlists, data_iter_maker, eval_env, NA_action)\u001b[0m\n\u001b[0;32m    691\u001b[0m     \u001b[1;31m# on some data to find out what type of data they return.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    692\u001b[0m     (num_column_counts,\n\u001b[1;32m--> 693\u001b[1;33m      \u001b[0mcat_levels_contrasts\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_examine_factor_types\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_factors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    694\u001b[0m                                                    \u001b[0mfactor_states\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    695\u001b[0m                                                    \u001b[0mdata_iter_maker\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\patsy\\build.py\u001b[0m in \u001b[0;36m_examine_factor_types\u001b[1;34m(factors, factor_states, data_iter_maker, NA_action)\u001b[0m\n\u001b[0;32m    441\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata_iter_maker\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    442\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mfactor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexamine_needed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 443\u001b[1;33m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfactor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfactor_states\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfactor\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    444\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mfactor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcat_sniffers\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mguess_categorical\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    445\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mfactor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcat_sniffers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\patsy\\eval.py\u001b[0m in \u001b[0;36meval\u001b[1;34m(self, memorize_state, data)\u001b[0m\n\u001b[0;32m    562\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    563\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmemorize_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 564\u001b[1;33m         return self._eval(memorize_state[\"eval_code\"],\n\u001b[0m\u001b[0;32m    565\u001b[0m                           \u001b[0mmemorize_state\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    566\u001b[0m                           data)\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\patsy\\eval.py\u001b[0m in \u001b[0;36m_eval\u001b[1;34m(self, code, memorize_state, data)\u001b[0m\n\u001b[0;32m    545\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_eval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmemorize_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    546\u001b[0m         \u001b[0minner_namespace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mVarLookupDict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmemorize_state\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"transforms\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 547\u001b[1;33m         return call_and_wrap_exc(\"Error evaluating factor\",\n\u001b[0m\u001b[0;32m    548\u001b[0m                                  \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    549\u001b[0m                                  \u001b[0mmemorize_state\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"eval_env\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\patsy\\compat.py\u001b[0m in \u001b[0;36mcall_and_wrap_exc\u001b[1;34m(msg, origin, f, *args, **kwargs)\u001b[0m\n\u001b[0;32m     41\u001b[0m                                  origin)\n\u001b[0;32m     42\u001b[0m             \u001b[1;31m# Use 'exec' to hide this syntax from the Python 2 parser:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m             \u001b[0mexec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"raise new_exc from e\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     44\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m             \u001b[1;31m# In python 2, we just let the original exception escape -- better\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\patsy\\compat.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;31mPatsyError\u001b[0m: Error evaluating factor: NameError: name 'overMt1' is not defined\n    change_votes ~ Mt + overMt1 + MtxoverMt1\n                        ^^^^^^^"
     ]
    }
   ],
   "source": [
    "reg = sm.formula.ols(\"change_votes ~ Mt + overMt1 + MtxoverMt1\", data = df_all).fit()\n",
    "print(\"\\n\\nMULTIVARIATE REGRESSION TABLE FOR TOTAL TB MORTALITY AND CITIES OVER 100000 PLUS SMALLER CITIES PLUS GROUPED:\")\n",
    "print(reg.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Mean of M(t): \" + str(np.mean(df_all[\"Mt\"])))\n",
    "print(\"1/2 SD of M(t): \" + str(np.std(df_all[\"Mt\"]) * .5) + \"\\n\")\n",
    "print(\"Mean of 1/M(t-1): \" + str(np.mean(df_all[\"overMt1\"])))\n",
    "print(\"1/2 SD of 1/M(t-1): \" + str(np.std(df_all[\"overMt1\"]) * .5) + \"\\n\")\n",
    "print(\"M(t)/M(t-1) when both are at their Mean: \" + str(np.mean(df_all[\"Mt\"]) * np.mean(df_all[\"overMt1\"])) + \"\\n\")\n",
    "\n",
    "print(\"Prediction with M(t) at mean, 1/M(t-1) at mean:\")\n",
    "print(-1.8041 + .0009 * np.mean(df_all[\"Mt\"]) + 65.8045 * np.mean(df_all[\"overMt1\"]) + 7.6466 * (np.mean(df_all[\"Mt\"]) * np.mean(df_all[\"overMt1\"])))\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Prediction with M(t) 1/2 SD below mean, 1/M(t-1) at mean:\")\n",
    "print(-1.8041 + .0009 * (np.mean(df_all[\"Mt\"]) - np.std(df_all[\"Mt\"]) * .5) + 65.8045 * np.mean(df_all[\"overMt1\"]) + 7.6466 * ((np.mean(df_all[\"Mt\"]) - np.std(df_all[\"Mt\"]) * .5) * np.mean(df_all[\"overMt1\"])))\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Prediction with M(t) 1/2 SD above mean, 1/M(t-1) at mean:\")\n",
    "print(-1.8041 + .0009 * (np.mean(df_all[\"Mt\"]) + np.std(df_all[\"Mt\"]) * .5) + 65.8045 * np.mean(df_all[\"overMt1\"]) + 7.6466 * ((np.mean(df_all[\"Mt\"]) + np.std(df_all[\"Mt\"]) * .5) * np.mean(df_all[\"overMt1\"])))\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Prediction with M(t) at mean, 1/M(t-1) 1/2 SD below mean:\")\n",
    "print(-1.8041 + .0009 * (np.mean(df_all[\"Mt\"])) + 65.8045 * (np.mean(df_all[\"overMt1\"]) - np.std(df_all[\"overMt1\"]) * .5) + 7.6466 * ((np.mean(df_all[\"Mt\"])) * (np.mean(df_all[\"overMt1\"]) - np.std(df_all[\"overMt1\"]) * .5)))\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Prediction with M(t) at mean, 1/M(t-1) 1/2 SD above mean:\")\n",
    "print(-1.8041 + .0009 * (np.mean(df_all[\"Mt\"])) + 65.8045 * (np.mean(df_all[\"overMt1\"]) + np.std(df_all[\"overMt1\"]) * .5) + 7.6466 * ((np.mean(df_all[\"Mt\"])) * (np.mean(df_all[\"overMt1\"]) + np.std(df_all[\"overMt1\"]) * .5)))\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(\"Interval with M(t) at mean, 1/M(t-1) at mean:\")\n",
    "#print(\"[\" + \n",
    "#      str(-13.777 + -.002 * np.mean(df_all[\"Mt\"]) + -682.578 * np.mean(df_all[\"overMt1\"]) + -.064 * (np.mean(df_all[\"Mt\"]) * np.mean(df_all[\"overMt1\"]))) + \n",
    "#      \", \" + \n",
    "#      str(10.169 + .004 * np.mean(df_all[\"Mt\"]) + 814.187 * np.mean(df_all[\"overMt1\"]) + 15.357 * (np.mean(df_all[\"Mt\"]) * np.mean(df_all[\"overMt1\"]))) + \n",
    "#      \"]\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
